In this paper, we devise and evaluate a new Grid-Based Priority Routing (GBPR) protocol for Underwater Wireless Sensor Networks (UWSNs). GBPR utilizes a 3D logical grid view of the monitored area to deliver
data packets to sink nodes. Particularly, data packets are forwarded on a cell-by-cell-basis using elected sensor nodes called cell-heads. The unique feature in GBPR is the lassification of the neighboring cells in
different priority levels according to their distances to the sink node. Cells closer to the sink are given higher priority to be selected as the next hop. This mechanism helps in reducing the number of hops; thus, reducing the energy consumption and end-to-end delay, and increasing the reliability. The protocol is
evaluated and compared against EMGGR and EEF protocols available in the literature. Simulation results show that GBPR outperforms the other two protocols in terms of energy efficiency, average delay and packet delivery ratio.
Wireless sensor network consists of hundreds to thousands of nodes that communicate among themselves
using radio signals and any node can leave or join the network when required. In Wireless sensor network no
central controller is present. Sensor nodes deployed in the network are responsible for data routing in the network.
Wireless sensor network is used to monitor the environmental conditions such as temperature, pressure, humidity,
sound, noise etc. Wireless Sensor nodes have very small size and have limited resources. In far places, it is very
difficult to recharge or replace the battery of the sensor nodes. In such conditions, focus is to reduce the battery
consumption of the sensor nodes. In this work, a new technique is proposed to enable efficient battery
consumption in a multicasting routing protocol. In this technique, the cluster heads are selected on the basis of
dynamic clustering using neural network. Simulation results show that the proposed technique is more reliable,
energy efficient and provide better results as compared to the existing technique.
Fuzzy based clustering and energy efficientIJCNCJournal
Underwater Wireless Sensor Network (UWSN) is a particular kind of sensor networks which is
characterized by using acoustic channels for communication. UWSN is challenged by great issues specially
the energy supply of sensor node which can be wasted rapidly by several factors. The most proposed
routing protocols for terrestrial sensor networks are not adequate for UWSN, thus new design of routing
protocols must be adapted to this constrain. In this paper we propose two new clustering algorithms based
on Fuzzy C-Means mechanisms. In the first proposition, the cluster head is elected initially based on the
closeness to the center of the cluster, then the node having the higher residual energy elects itself as a
cluster head. All non-cluster head nodes transmit sensed data to the cluster head. This latter performs data
aggregation and transmits the data directly to the base station. The second algorithm uses the same
principle in forming clusters and electing cluster heads but operates in multi-hop mode to forward data
from cluster heads to the underwater sink (uw-sink). Furthermore the two proposed algorithms are tested
for static and dynamic deployment. Simulation results demonstrate the effectiveness of the proposed
algorithms resulting in an extension of the network lifetime.
ADAPTIVE AODV ROUTING PROTOCOL FOR MOBILE ADHOC NETWORKSijasuc
This document proposes techniques to improve the efficiency of route request flooding in mobile ad hoc networks. It presents two new enhancements to the Ad-hoc On-Demand Distance Vector (AODV) routing protocol: EAODV1, which selects neighboring nodes to forward route requests based on mobility and recent usage for moderate node speeds; and EAODV2, which alternates between flooding and selection based on mobility and usage for high speeds. It also introduces Adaptive AODV (AAODV), which automatically switches between EAODV1 and EAODV2 based on measured node mobility. Simulation results show these methods reduce overhead, improve packet delivery ratio, and reduce end-to-end delay compared to standard AODV
Clustering effects on wireless mobile ad hoc networks performancesijcsit
A new era is dawning for wireless mobile ad hoc networks where communication will be done using a
group of mobile devices called cluster, hence clustered network. In a clustered network, protocols used by
these mobile devices are different from those used in a wired network; which helps to save computation
time and resources efficiently. This paper focuses on Cluster-Based Routing Protocol and Dynamic Source
Routing. The results presented in this paper illustrates the implementation of Ad-hoc On-Demand Distance
Vector routing protocol for enhancing mobile nodes performance and lifetime in a clustered network and to
demonstrate how this routing protocol results in time efficient and resource saving in wireless mobile ad
hoc networks.
ENERGY LOCATION AWARE ROUTING PROTOCOL (ELARP) FOR WIRELESS MULTIMEDIA SENSOR...ijcsit
Wireless Sensor Networks (WSNs)have sensor nodes that sense and extract information from surrounding
environment, processing information locally then transmit it to sink wirelessly. Multimedia data is larger in
volume than scalar data, thus transmitting multimedia data via Wireless Multimedia Sensor Networks
(WMSNs) requires stick constraints on quality of services in terms of energy, throughput and end to end
delay.Multipath routing is to discover multipath during route discovery from source to sink. Discover
multipath and sending data via these different paths improve the bandwidth and decrease the end to end
delay. This paper introduces an Energy Location Aware Routing Protocol (ELARP) which is reactive
multipath routing protocol establishing three paths with awareness of node’s residual energy and distance.
ELARP has experimented with NS2 simulator. The simulation results show that ELARP enhances QoS for
multimedia data in terms of end to end delay and packet delivery ratio.
ENERGY EFFICIENT MULTICAST ROUTING IN MANET ijac journal
In this paper, we have presented the Modified Multicasting through Time Reservation using Adaptive
Control for Excellent Energy efficiency (MMC-TRACE). It is a real time multicasting architecture for
Mobile Ad-Hoc networks to make their work an energy efficient one .MMC-TRACE is a cross layer design
where the network layer and medium access control layer functionality are done in a single integrated
layer design. The basic design of the architecture is to establish and maintain an active multicast tree
surrounded by a passive mesh within a mobile ad hoc network. Energy efficiency is maximized by enabling
the particular node from sleep to awake mode while the remaining nodes of the same path are maintained
at sleep mode. Energy efficiency too achieved by eliminating most of the redundant data receptions across
nodes. The performance of MMC-TRACE are evaluated with the help of ns-2 simulations and comparisons
are made with its predecessor such as MC-TRACE. The results show that the MMC-TRACE provides
superior energy efficiency, competitive QoS performance and bandwidth efficiency.
ENERGY-EFFICIENT MULTI-HOP ROUTING WITH UNEQUAL CLUSTERING APPROACH FOR WIREL...IJCNCJournal
This document summarizes a research paper that proposes a new routing protocol called Energy-efficient Multi-hop routing with Unequal Clustering (EMUC) for wireless sensor networks. EMUC aims to balance energy consumption between nodes and extend network lifetime by using unequal clustering and multi-hop communication. It creates clusters of different sizes based on distance from the base station. Data is transmitted from cluster members to heads, and between heads to the base station, using multiple hops to reduce transmission costs. Simulation results show EMUC balances energy usage, mitigates hotspot issues, and significantly prolongs network lifetime compared to other clustering routing protocols.
Wireless sensor network consists of hundreds to thousands of nodes that communicate among themselves
using radio signals and any node can leave or join the network when required. In Wireless sensor network no
central controller is present. Sensor nodes deployed in the network are responsible for data routing in the network.
Wireless sensor network is used to monitor the environmental conditions such as temperature, pressure, humidity,
sound, noise etc. Wireless Sensor nodes have very small size and have limited resources. In far places, it is very
difficult to recharge or replace the battery of the sensor nodes. In such conditions, focus is to reduce the battery
consumption of the sensor nodes. In this work, a new technique is proposed to enable efficient battery
consumption in a multicasting routing protocol. In this technique, the cluster heads are selected on the basis of
dynamic clustering using neural network. Simulation results show that the proposed technique is more reliable,
energy efficient and provide better results as compared to the existing technique.
Fuzzy based clustering and energy efficientIJCNCJournal
Underwater Wireless Sensor Network (UWSN) is a particular kind of sensor networks which is
characterized by using acoustic channels for communication. UWSN is challenged by great issues specially
the energy supply of sensor node which can be wasted rapidly by several factors. The most proposed
routing protocols for terrestrial sensor networks are not adequate for UWSN, thus new design of routing
protocols must be adapted to this constrain. In this paper we propose two new clustering algorithms based
on Fuzzy C-Means mechanisms. In the first proposition, the cluster head is elected initially based on the
closeness to the center of the cluster, then the node having the higher residual energy elects itself as a
cluster head. All non-cluster head nodes transmit sensed data to the cluster head. This latter performs data
aggregation and transmits the data directly to the base station. The second algorithm uses the same
principle in forming clusters and electing cluster heads but operates in multi-hop mode to forward data
from cluster heads to the underwater sink (uw-sink). Furthermore the two proposed algorithms are tested
for static and dynamic deployment. Simulation results demonstrate the effectiveness of the proposed
algorithms resulting in an extension of the network lifetime.
ADAPTIVE AODV ROUTING PROTOCOL FOR MOBILE ADHOC NETWORKSijasuc
This document proposes techniques to improve the efficiency of route request flooding in mobile ad hoc networks. It presents two new enhancements to the Ad-hoc On-Demand Distance Vector (AODV) routing protocol: EAODV1, which selects neighboring nodes to forward route requests based on mobility and recent usage for moderate node speeds; and EAODV2, which alternates between flooding and selection based on mobility and usage for high speeds. It also introduces Adaptive AODV (AAODV), which automatically switches between EAODV1 and EAODV2 based on measured node mobility. Simulation results show these methods reduce overhead, improve packet delivery ratio, and reduce end-to-end delay compared to standard AODV
Clustering effects on wireless mobile ad hoc networks performancesijcsit
A new era is dawning for wireless mobile ad hoc networks where communication will be done using a
group of mobile devices called cluster, hence clustered network. In a clustered network, protocols used by
these mobile devices are different from those used in a wired network; which helps to save computation
time and resources efficiently. This paper focuses on Cluster-Based Routing Protocol and Dynamic Source
Routing. The results presented in this paper illustrates the implementation of Ad-hoc On-Demand Distance
Vector routing protocol for enhancing mobile nodes performance and lifetime in a clustered network and to
demonstrate how this routing protocol results in time efficient and resource saving in wireless mobile ad
hoc networks.
ENERGY LOCATION AWARE ROUTING PROTOCOL (ELARP) FOR WIRELESS MULTIMEDIA SENSOR...ijcsit
Wireless Sensor Networks (WSNs)have sensor nodes that sense and extract information from surrounding
environment, processing information locally then transmit it to sink wirelessly. Multimedia data is larger in
volume than scalar data, thus transmitting multimedia data via Wireless Multimedia Sensor Networks
(WMSNs) requires stick constraints on quality of services in terms of energy, throughput and end to end
delay.Multipath routing is to discover multipath during route discovery from source to sink. Discover
multipath and sending data via these different paths improve the bandwidth and decrease the end to end
delay. This paper introduces an Energy Location Aware Routing Protocol (ELARP) which is reactive
multipath routing protocol establishing three paths with awareness of node’s residual energy and distance.
ELARP has experimented with NS2 simulator. The simulation results show that ELARP enhances QoS for
multimedia data in terms of end to end delay and packet delivery ratio.
ENERGY EFFICIENT MULTICAST ROUTING IN MANET ijac journal
In this paper, we have presented the Modified Multicasting through Time Reservation using Adaptive
Control for Excellent Energy efficiency (MMC-TRACE). It is a real time multicasting architecture for
Mobile Ad-Hoc networks to make their work an energy efficient one .MMC-TRACE is a cross layer design
where the network layer and medium access control layer functionality are done in a single integrated
layer design. The basic design of the architecture is to establish and maintain an active multicast tree
surrounded by a passive mesh within a mobile ad hoc network. Energy efficiency is maximized by enabling
the particular node from sleep to awake mode while the remaining nodes of the same path are maintained
at sleep mode. Energy efficiency too achieved by eliminating most of the redundant data receptions across
nodes. The performance of MMC-TRACE are evaluated with the help of ns-2 simulations and comparisons
are made with its predecessor such as MC-TRACE. The results show that the MMC-TRACE provides
superior energy efficiency, competitive QoS performance and bandwidth efficiency.
ENERGY-EFFICIENT MULTI-HOP ROUTING WITH UNEQUAL CLUSTERING APPROACH FOR WIREL...IJCNCJournal
This document summarizes a research paper that proposes a new routing protocol called Energy-efficient Multi-hop routing with Unequal Clustering (EMUC) for wireless sensor networks. EMUC aims to balance energy consumption between nodes and extend network lifetime by using unequal clustering and multi-hop communication. It creates clusters of different sizes based on distance from the base station. Data is transmitted from cluster members to heads, and between heads to the base station, using multiple hops to reduce transmission costs. Simulation results show EMUC balances energy usage, mitigates hotspot issues, and significantly prolongs network lifetime compared to other clustering routing protocols.
Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading i...1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
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DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
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3. ASP
4. VB
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Multihop Multi-Channel Distributed QOS Scheduling MAC Scheme for Wireless Sen...IOSR Journals
This document proposes a Multihop Multi-Channel Distributed QoS Scheduling MAC scheme (MMDQS-MAC) to improve the performance of wireless sensor networks. MMDQS-MAC supports dynamic channel assignment where each sensor node is equipped with a directional antenna. It aims to decrease collisions and interference, improve overall network performance, and is suitable for low traffic networks. Simulation results show that MMDQS-MAC improves aggregate throughput, transmission success rate, packet delivery ratio, energy efficiency, and end-to-end delay.
The document presents the outline of a research project on performance evaluation of secure data transmission in wireless sensor networks using IEEE 802.11x standards. The research aims to enhance network lifetime by designing an energy-efficient clustering approach and data aggregation technique. It involves developing a cluster head selection algorithm using genetic algorithms, designing a broadcast tree construction protocol for data transmission, and implementing hash-based authentication. The research will be conducted in phases involving literature review, methodology development, implementation, and performance evaluation. The expected outcomes include reduced data transmission time and improved quality of service through increased network lifetime.
A comparative study in wireless sensor networksijwmn
This document summarizes and compares several routing algorithms proposed for wireless sensor networks. It discusses algorithms that aim to improve reliability, power efficiency, lifetime, and fault tolerance. The evaluation section compares how each algorithm addresses challenges like reliability, energy conservation, and adapting to topology changes. While various algorithms achieve improvements in areas like power efficiency and lifetime, most still have limitations and do not fully address all the key challenges for wireless sensor networks.
A FASTER ROUTING SCHEME FOR STATIONARY WIRELESS SENSOR NETWORKS - A HYBRID AP...ijasuc
A wireless sensor network consists of light-weight, low power, small size sensor nodes. Routing in wireless
sensor networks is a demanding task. This demand has led to a number of routing protocols which
efficiently utilize the limited resources available at the sensor nodes. Most of these protocols are either
based on single hop routing or multi hop routing and typically find the minimum energy path without
addressing other issues such as time delay in delivering a packet, load balancing, and redundancy of data.
Response time is very critical in environment monitoring sensor networks where typically the sensors are
stationary and transmit data to a base station or a sink node. In this paper a faster load balancing routing
protocol based on location with a hybrid approach is proposed.
Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...IDES Editor
Wireless sensor network (WSN) is emerging as a
major research field in computer networks over the last decade
due to its wide variety of embedded real time applications.
Sensor networks have infrastructure-less architecture because
of frequently varying topology and link status. Routing is an
extremely challenging task for battery-powered resourceconstrained
WSN, since it is main cause for energy depletion
and energy must be utilized prudently to enhance lifetime
for sensor networks. This drives a myriad of research efforts
aiming at efficient data dissemination. In this paper we
analyze how efficiently MANET specific routing protocols
OLSR (Optimized Link-State Routing protocol), DYMO
(Dynamic MANET On-demand) and ZRP (Zone Routing
Protocol) perform in IEEE 802.15.4 enabled wireless sensor
networks and evaluate their simulation results using Qualnet
simulator. Several simulations were carried out under varying
network size and offered load for performance evaluation and
relative comparison of protocols is reported in terms of average
end to end delay, throughput and jitter.
Mtadf multi hop traffic aware data for warding for congestion control in wir...ijwmn
The document summarizes a proposed algorithm called MTADF (Multi Hop Traffic-Aware Data Forwarding) for congestion control in wireless sensor networks. The algorithm uses two potential fields - depth potential field and queue length potential field - to route data packets around congested areas along multiple paths. This helps distribute traffic more evenly and utilize less busy nodes, reducing packet drops and improving throughput compared to existing one-hop routing algorithms. The algorithm constructs the two potential fields independently and then combines them to make dynamic forwarding decisions for data packets. Simulations show the MTADF algorithm performs better than previous work in mitigating congestion.
We make use of the existence of cell-disjoint paths in the 3D grid topology to design a new highly reliable adaptive geographic routing protocol called Grid-based Adaptive Routing Protocol (GARP) for Underwater Wireless Sensor Networks. In GARP, the underwater environment is viewed as a virtual 3D grid of cells. A packet is forwarded following a pre-constructed routing path from a node in a grid cell to a node in a neighbouring grid cell repeatedly until the destination sink node is reached. When a selected routing path becomes unavailable, GARP adapts to the condition by switching to an alternative path making use of the existing cell-disjoint paths. Since the protocol uses pre-constructed routing paths, it avoids path establishment and path maintenance overheads. Analytical performance evaluation results for GARP are obtained showing its high reliability. In tested cases, the delivery ratio has approached 100% when the network density has reached a minimum number of sensor nodes per grid cell.
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
The document discusses clustering routing protocols for wireless sensor networks. It provides an overview of clustering techniques which group sensor nodes into clusters with elected cluster heads that aggregate and transmit data to the base station. This approach provides benefits like improved scalability, reduced energy consumption and load compared to flat routing protocols. The document also outlines various objectives of clustering like data aggregation, load balancing, fault tolerance and connectivity. It reviews several popular clustering protocols and notes that no single technique performs best in all areas, leaving room for future improvements to address these issues.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that open the door of pleasing plenty of researchers during this field of study. In several under water based sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility of each sensor nodes are measured through the water atmosphere from the water flow for sensor based protocol formations. Researchers have developed many routing protocols. However, those lost their charm with the time. This can be the demand of the age to supply associate degree upon energy-efficient and ascendable strong routing protocol for under water actuator networks. During this work, the authors tend to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use of total energy consumption and ensures packet transmission which redirects as an additional reliability in compare to different routing protocols. In this work, the authors have used the level of forwarding node, residual energy and distance from the forwarding node to the causing node as a proof in multicasting technique comparisons. Throughout this work, the authors have got a recognition result concerning about 86.35% on the average in node multicasting performances. Simulation has been experienced each in a wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned protocol.
Mobile ad hoc network become nowadays more and more used in different
domains, due to its flexibility and low cost of deployment. However, this
kind of network still suffering from several problems as the lack of resources.
Many solutions are proposed to face these problems, among these solutions
there is the clustering approach. This approach tries to partition the network
into a virtual group. It is considered as a primordial solution that aims to
enhance the performance of the total network, and makes it possible to
guarantee basic levels of system performance. In this paper, we study some
schemes of clustering such as Dominating-Set-based clustering, Energyefficient
clustering, Low-maintenance clustering, Load-balancing clustering,
and Combined-metrics based clustering.
Wireless Sensor Networks (WSNs)have sensor nodes that sense and extract information from surrounding environment, processing information locally then transmit it to sink wirelessly. Multimedia data is larger in volume than scalar data, thus transmitting multimedia data via Wireless Multimedia Sensor Networks (WMSNs) requires stick constraints on quality of services in terms of energy, throughput and end to end delay.Multipath routing is to discover multipath during route discovery from source to sink. Discover multipath and sending data via these different paths improve the bandwidth and decrease the end to end delay. This paper introduces an Energy Location Aware Routing Protocol (ELARP) which is reactive multipath routing protocol establishing three paths with awareness of node’s residual energy and distance. ELARP has experimented with NS2 simulator. The simulation results show that ELARP enhances QoS for multimedia data in terms of end to end delay and packet delivery ratio.
Wireless Sensor Networks (WSNs)have sensor nodes that sense and extract information from surrounding environment, processing information locally then transmit it to sink wirelessly. Multimedia data is larger in volume than scalar data, thus transmitting multimedia data via Wireless Multimedia Sensor Networks (WMSNs) requires stick constraints on quality of services in terms of energy, throughput and end to end delay.Multipath routing is to discover multipath during route discovery from source to sink. Discover multipath and sending data via these different paths improve the bandwidth and decrease the end to end delay. This paper introduces an Energy Location Aware Routing Protocol (ELARP) which is reactive multipath routing protocol establishing three paths with awareness of node’s residual energy and distance. ELARP has experimented with NS2 simulator. The simulation results show that ELARP enhances QoS for multimedia data in terms of end to end delay and packet delivery ratio.
The present paper describes a novel Raspberry Pi and Arduino UNO architecture used as a meteorological station. One of the advantages of the proposed architecture is the huge quantity of sensors developed for its usage; practically one can find them for any application, and weather sensing is not an exception. The principle followed is to configure Raspberry as a collector for measures obtained from Arduino, transmitting occurs via USB; meanwhile, Raspberry broadcasts them via a web page. For such activity is possible thanks to Raspbian, a Linux-based operating system. It has a lot of libraries and resources available, among them Apache Web Server, that gives the possibility to host a web-page. On it, the user can observe temperature, humidity, solar radiance, and wind speed and direction. Information on the web-page is refreshed each five minute; however, measurements arrive at Raspberry every ten seconds. This low refreshment rate was determined because weather variables normally do not abruptly change. As an additional feature, system stores all information on the log file, this gives the possibility for future analysis and processing.
Wireless Sensor Networks (WSNs)have sensor nodes that sense and extract information from surrounding environment, processing information locally then transmit it to sink wirelessly. Multimedia data is larger in volume than scalar data, thus transmitting multimedia data via Wireless Multimedia Sensor Networks (WMSNs) requires stick constraints on quality of services in terms of energy, throughput and end to end delay. Multipath routing is to discover multipath during route discovery from source to sink. Discover multipath and sending data via these different paths improve the bandwidth and decrease the end to end delay. This paper introduces an Energy Location Aware Routing Protocol (ELARP) which is reactive multipath routing protocol establishing three paths with awareness of node’s residual energy and distance. ELARP has experimented with NS2 simulator. The simulation results show that ELARP enhances QoS for multimedia data in terms of end to end delay and packet delivery ratio.
VHFRP: Virtual Hexagonal Frame Routing Protocol for Wireless Sensor NetworkIJCNCJournal
As physical and digital worlds become increasingly intertwined, wireless sensor networks are becoming an indispensable technology. A mobile sink may be required for some applications in the sensor field, where incomplete and/or delayed data delivery can lead to inappropriate conclusions. Therefore, latency and packet delivery ratios must be of high quality. In most existing schemes, mobile sinks are used to extend network lifetimes. By partitioning the sensor field into k equal sized frames, the proposed scheme creates a virtual hexagonal structure. Each frame header (FH) is linked together through the creation of a virtual backbone network. Frame headers are assigned to nodes near the centre of each frame. The virtual backbone network enables data collection from members of the frame and delivers it to the mobile sink. The proposed Virtual Hexagonal Frame Routing Protocol (VHFRP) improves throughput by 25%, energy consumption by 30% and delay by 9% as compared with static sink scenario.
VHFRP: VIRTUAL HEXAGONAL FRAME ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKIJCNCJournal
As physical and digital worlds become increasingly intertwined, wireless sensor networks are becoming an
indispensable technology. A mobile sink may be required for some applications in the sensor field, where
incomplete and/or delayed data delivery can lead to inappropriate conclusions. Therefore, latency and
packet delivery ratios must be of high quality. In most existing schemes, mobile sinks are used to extend
network lifetimes. By partitioning the sensor field into k equal sized frames, the proposed scheme creates a
virtual hexagonal structure. Each frame header (FH) is linked together through the creation of a virtual
backbone network. Frame headers are assigned to nodes near the centre of each frame. The virtual
backbone network enables data collection from members of the frame and delivers it to the mobile sink.
The proposed Virtual Hexagonal Frame Routing Protocol (VHFRP) improves throughput by 25%, energy
consumption by 30% and delay by 9% as compared with static sink scenario.
Clustering and data aggregation scheme in underwater wireless acoustic sensor...TELKOMNIKA JOURNAL
Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in
researchers due to its wide area of applications. To extract the data from underwater and transmit to
watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of
clustering and data aggregation schemes are to decrease the consumption of energy and prolong the
lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their
geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its
cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the
cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and
discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm.
The simulation results indicate that the proposed scheme performs better in maximizing network lifetime
and minimizing energy consumption.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that open the door of pleasing plenty of researchers during this field of study. In several under water based
sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility of each sensor nodes are measured through the water atmosphere from the water flow for sensor based protocol formations. Researchers have developed many routing protocols. However, those lost their charm with the time. This can be the demand of the age to supply associate degree upon energy-efficient and ascendable strong routing protocol for under water actuator networks. During this work, the authors tend to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use of total energy consumption and ensures packet transmission which redirects as an additional reliability in compare to different routing protocols. In this work, the authors have used the level of forwarding node, residual energy and distance from the forwarding node to the causing node as a proof in multicasting technique comparisons. Throughout this work, the authors have got a recognition result concerning about
86.35% on the average in node multicasting performances. Simulation has been experienced each in a wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned protocol.
Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading i...1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
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Multihop Multi-Channel Distributed QOS Scheduling MAC Scheme for Wireless Sen...IOSR Journals
This document proposes a Multihop Multi-Channel Distributed QoS Scheduling MAC scheme (MMDQS-MAC) to improve the performance of wireless sensor networks. MMDQS-MAC supports dynamic channel assignment where each sensor node is equipped with a directional antenna. It aims to decrease collisions and interference, improve overall network performance, and is suitable for low traffic networks. Simulation results show that MMDQS-MAC improves aggregate throughput, transmission success rate, packet delivery ratio, energy efficiency, and end-to-end delay.
The document presents the outline of a research project on performance evaluation of secure data transmission in wireless sensor networks using IEEE 802.11x standards. The research aims to enhance network lifetime by designing an energy-efficient clustering approach and data aggregation technique. It involves developing a cluster head selection algorithm using genetic algorithms, designing a broadcast tree construction protocol for data transmission, and implementing hash-based authentication. The research will be conducted in phases involving literature review, methodology development, implementation, and performance evaluation. The expected outcomes include reduced data transmission time and improved quality of service through increased network lifetime.
A comparative study in wireless sensor networksijwmn
This document summarizes and compares several routing algorithms proposed for wireless sensor networks. It discusses algorithms that aim to improve reliability, power efficiency, lifetime, and fault tolerance. The evaluation section compares how each algorithm addresses challenges like reliability, energy conservation, and adapting to topology changes. While various algorithms achieve improvements in areas like power efficiency and lifetime, most still have limitations and do not fully address all the key challenges for wireless sensor networks.
A FASTER ROUTING SCHEME FOR STATIONARY WIRELESS SENSOR NETWORKS - A HYBRID AP...ijasuc
A wireless sensor network consists of light-weight, low power, small size sensor nodes. Routing in wireless
sensor networks is a demanding task. This demand has led to a number of routing protocols which
efficiently utilize the limited resources available at the sensor nodes. Most of these protocols are either
based on single hop routing or multi hop routing and typically find the minimum energy path without
addressing other issues such as time delay in delivering a packet, load balancing, and redundancy of data.
Response time is very critical in environment monitoring sensor networks where typically the sensors are
stationary and transmit data to a base station or a sink node. In this paper a faster load balancing routing
protocol based on location with a hybrid approach is proposed.
Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...IDES Editor
Wireless sensor network (WSN) is emerging as a
major research field in computer networks over the last decade
due to its wide variety of embedded real time applications.
Sensor networks have infrastructure-less architecture because
of frequently varying topology and link status. Routing is an
extremely challenging task for battery-powered resourceconstrained
WSN, since it is main cause for energy depletion
and energy must be utilized prudently to enhance lifetime
for sensor networks. This drives a myriad of research efforts
aiming at efficient data dissemination. In this paper we
analyze how efficiently MANET specific routing protocols
OLSR (Optimized Link-State Routing protocol), DYMO
(Dynamic MANET On-demand) and ZRP (Zone Routing
Protocol) perform in IEEE 802.15.4 enabled wireless sensor
networks and evaluate their simulation results using Qualnet
simulator. Several simulations were carried out under varying
network size and offered load for performance evaluation and
relative comparison of protocols is reported in terms of average
end to end delay, throughput and jitter.
Mtadf multi hop traffic aware data for warding for congestion control in wir...ijwmn
The document summarizes a proposed algorithm called MTADF (Multi Hop Traffic-Aware Data Forwarding) for congestion control in wireless sensor networks. The algorithm uses two potential fields - depth potential field and queue length potential field - to route data packets around congested areas along multiple paths. This helps distribute traffic more evenly and utilize less busy nodes, reducing packet drops and improving throughput compared to existing one-hop routing algorithms. The algorithm constructs the two potential fields independently and then combines them to make dynamic forwarding decisions for data packets. Simulations show the MTADF algorithm performs better than previous work in mitigating congestion.
We make use of the existence of cell-disjoint paths in the 3D grid topology to design a new highly reliable adaptive geographic routing protocol called Grid-based Adaptive Routing Protocol (GARP) for Underwater Wireless Sensor Networks. In GARP, the underwater environment is viewed as a virtual 3D grid of cells. A packet is forwarded following a pre-constructed routing path from a node in a grid cell to a node in a neighbouring grid cell repeatedly until the destination sink node is reached. When a selected routing path becomes unavailable, GARP adapts to the condition by switching to an alternative path making use of the existing cell-disjoint paths. Since the protocol uses pre-constructed routing paths, it avoids path establishment and path maintenance overheads. Analytical performance evaluation results for GARP are obtained showing its high reliability. In tested cases, the delivery ratio has approached 100% when the network density has reached a minimum number of sensor nodes per grid cell.
GREEDY CLUSTER BASED ROUTING FOR WIRELESS SENSOR NETWORKSijcsit
In recent years, applications of wireless sensor networks have evolved in many areas such as target tracking, environmental monitoring, military and medical applications. Wireless sensor network continuously collect and send data through sensor nodes from a specific region to a base station. But, data redundancy due to neighbouring sensors consumes energy, compromising the network lifetime. In order to improve the network lifetime, a novel cluster based local route search method, called, Greedy Clusterbased Routing (GCR) technique in wireless sensor network. The proposed GCR method uses arbitrary timer in order to participate cluster head selection process with maximum neighbour nodes and minimum distance between the source and base station. GCR constructs dynamic routing improving the rate of network lifetime through Mass Proportion value. Also, GCR uses a greedy route finding strategy for
balancing energy consumption. Experimental results show that GCR achieves significant energy savings and prolong network lifetime.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
The document discusses clustering routing protocols for wireless sensor networks. It provides an overview of clustering techniques which group sensor nodes into clusters with elected cluster heads that aggregate and transmit data to the base station. This approach provides benefits like improved scalability, reduced energy consumption and load compared to flat routing protocols. The document also outlines various objectives of clustering like data aggregation, load balancing, fault tolerance and connectivity. It reviews several popular clustering protocols and notes that no single technique performs best in all areas, leaving room for future improvements to address these issues.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that open the door of pleasing plenty of researchers during this field of study. In several under water based sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility of each sensor nodes are measured through the water atmosphere from the water flow for sensor based protocol formations. Researchers have developed many routing protocols. However, those lost their charm with the time. This can be the demand of the age to supply associate degree upon energy-efficient and ascendable strong routing protocol for under water actuator networks. During this work, the authors tend to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use of total energy consumption and ensures packet transmission which redirects as an additional reliability in compare to different routing protocols. In this work, the authors have used the level of forwarding node, residual energy and distance from the forwarding node to the causing node as a proof in multicasting technique comparisons. Throughout this work, the authors have got a recognition result concerning about 86.35% on the average in node multicasting performances. Simulation has been experienced each in a wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned protocol.
Mobile ad hoc network become nowadays more and more used in different
domains, due to its flexibility and low cost of deployment. However, this
kind of network still suffering from several problems as the lack of resources.
Many solutions are proposed to face these problems, among these solutions
there is the clustering approach. This approach tries to partition the network
into a virtual group. It is considered as a primordial solution that aims to
enhance the performance of the total network, and makes it possible to
guarantee basic levels of system performance. In this paper, we study some
schemes of clustering such as Dominating-Set-based clustering, Energyefficient
clustering, Low-maintenance clustering, Load-balancing clustering,
and Combined-metrics based clustering.
Wireless Sensor Networks (WSNs)have sensor nodes that sense and extract information from surrounding environment, processing information locally then transmit it to sink wirelessly. Multimedia data is larger in volume than scalar data, thus transmitting multimedia data via Wireless Multimedia Sensor Networks (WMSNs) requires stick constraints on quality of services in terms of energy, throughput and end to end delay.Multipath routing is to discover multipath during route discovery from source to sink. Discover multipath and sending data via these different paths improve the bandwidth and decrease the end to end delay. This paper introduces an Energy Location Aware Routing Protocol (ELARP) which is reactive multipath routing protocol establishing three paths with awareness of node’s residual energy and distance. ELARP has experimented with NS2 simulator. The simulation results show that ELARP enhances QoS for multimedia data in terms of end to end delay and packet delivery ratio.
Wireless Sensor Networks (WSNs)have sensor nodes that sense and extract information from surrounding environment, processing information locally then transmit it to sink wirelessly. Multimedia data is larger in volume than scalar data, thus transmitting multimedia data via Wireless Multimedia Sensor Networks (WMSNs) requires stick constraints on quality of services in terms of energy, throughput and end to end delay.Multipath routing is to discover multipath during route discovery from source to sink. Discover multipath and sending data via these different paths improve the bandwidth and decrease the end to end delay. This paper introduces an Energy Location Aware Routing Protocol (ELARP) which is reactive multipath routing protocol establishing three paths with awareness of node’s residual energy and distance. ELARP has experimented with NS2 simulator. The simulation results show that ELARP enhances QoS for multimedia data in terms of end to end delay and packet delivery ratio.
The present paper describes a novel Raspberry Pi and Arduino UNO architecture used as a meteorological station. One of the advantages of the proposed architecture is the huge quantity of sensors developed for its usage; practically one can find them for any application, and weather sensing is not an exception. The principle followed is to configure Raspberry as a collector for measures obtained from Arduino, transmitting occurs via USB; meanwhile, Raspberry broadcasts them via a web page. For such activity is possible thanks to Raspbian, a Linux-based operating system. It has a lot of libraries and resources available, among them Apache Web Server, that gives the possibility to host a web-page. On it, the user can observe temperature, humidity, solar radiance, and wind speed and direction. Information on the web-page is refreshed each five minute; however, measurements arrive at Raspberry every ten seconds. This low refreshment rate was determined because weather variables normally do not abruptly change. As an additional feature, system stores all information on the log file, this gives the possibility for future analysis and processing.
Wireless Sensor Networks (WSNs)have sensor nodes that sense and extract information from surrounding environment, processing information locally then transmit it to sink wirelessly. Multimedia data is larger in volume than scalar data, thus transmitting multimedia data via Wireless Multimedia Sensor Networks (WMSNs) requires stick constraints on quality of services in terms of energy, throughput and end to end delay. Multipath routing is to discover multipath during route discovery from source to sink. Discover multipath and sending data via these different paths improve the bandwidth and decrease the end to end delay. This paper introduces an Energy Location Aware Routing Protocol (ELARP) which is reactive multipath routing protocol establishing three paths with awareness of node’s residual energy and distance. ELARP has experimented with NS2 simulator. The simulation results show that ELARP enhances QoS for multimedia data in terms of end to end delay and packet delivery ratio.
VHFRP: Virtual Hexagonal Frame Routing Protocol for Wireless Sensor NetworkIJCNCJournal
As physical and digital worlds become increasingly intertwined, wireless sensor networks are becoming an indispensable technology. A mobile sink may be required for some applications in the sensor field, where incomplete and/or delayed data delivery can lead to inappropriate conclusions. Therefore, latency and packet delivery ratios must be of high quality. In most existing schemes, mobile sinks are used to extend network lifetimes. By partitioning the sensor field into k equal sized frames, the proposed scheme creates a virtual hexagonal structure. Each frame header (FH) is linked together through the creation of a virtual backbone network. Frame headers are assigned to nodes near the centre of each frame. The virtual backbone network enables data collection from members of the frame and delivers it to the mobile sink. The proposed Virtual Hexagonal Frame Routing Protocol (VHFRP) improves throughput by 25%, energy consumption by 30% and delay by 9% as compared with static sink scenario.
VHFRP: VIRTUAL HEXAGONAL FRAME ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKIJCNCJournal
As physical and digital worlds become increasingly intertwined, wireless sensor networks are becoming an
indispensable technology. A mobile sink may be required for some applications in the sensor field, where
incomplete and/or delayed data delivery can lead to inappropriate conclusions. Therefore, latency and
packet delivery ratios must be of high quality. In most existing schemes, mobile sinks are used to extend
network lifetimes. By partitioning the sensor field into k equal sized frames, the proposed scheme creates a
virtual hexagonal structure. Each frame header (FH) is linked together through the creation of a virtual
backbone network. Frame headers are assigned to nodes near the centre of each frame. The virtual
backbone network enables data collection from members of the frame and delivers it to the mobile sink.
The proposed Virtual Hexagonal Frame Routing Protocol (VHFRP) improves throughput by 25%, energy
consumption by 30% and delay by 9% as compared with static sink scenario.
Clustering and data aggregation scheme in underwater wireless acoustic sensor...TELKOMNIKA JOURNAL
Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in
researchers due to its wide area of applications. To extract the data from underwater and transmit to
watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of
clustering and data aggregation schemes are to decrease the consumption of energy and prolong the
lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their
geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its
cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the
cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and
discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm.
The simulation results indicate that the proposed scheme performs better in maximizing network lifetime
and minimizing energy consumption.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that open the door of pleasing plenty of researchers during this field of study. In several under water based
sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility of each sensor nodes are measured through the water atmosphere from the water flow for sensor based protocol formations. Researchers have developed many routing protocols. However, those lost their charm with the time. This can be the demand of the age to supply associate degree upon energy-efficient and ascendable strong routing protocol for under water actuator networks. During this work, the authors tend to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use of total energy consumption and ensures packet transmission which redirects as an additional reliability in compare to different routing protocols. In this work, the authors have used the level of forwarding node, residual energy and distance from the forwarding node to the causing node as a proof in multicasting technique comparisons. Throughout this work, the authors have got a recognition result concerning about
86.35% on the average in node multicasting performances. Simulation has been experienced each in a wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned protocol.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that
open the door of pleasing plenty of researchers during this field of study. In several under water based
sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility
of each sensor nodes are measured through the water atmosphere from the water flow for sensor based
protocol formations. Researchers have developed many routing protocols. However, those lost their charm
with the time. This can be the demand of the age to supply associate degree upon energy-efficient and
ascendable strong routing protocol for under water actuator networks. During this work, the authors tend
to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to
offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use
of total energy consumption and ensures packet transmission which redirects as an additional reliability in
compare to different routing protocols. In this work, the authors have used the level of forwarding node,
residual energy and distance from the forwarding node to the causing node as a proof in multicasting
technique comparisons. Throughout this work, the authors have got a recognition result concerning about
86.35% on the average in node multicasting performances. Simulation has been experienced each in a
wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned
protocol.
An energy aware scheme for layered chain in underwater wireless sensor networ...IJECEIAES
Extending the network lifetime is a very challenging problem that needs to be taken into account during routing data in wireless sensor networks in general and particularly in underwater wireless sensor networks (UWSN). For this purpose, the present paper proposes a multilayer chain based on genetic algorithm routing (MCGA) for routing data from nodes to the sink. This algorithm consists to create a limited number of local chains constructed by using genetic algorithm in order to obtain the shortest path between nodes; furthermore, a leader node (LN) is elected in each chain followed by constructing a global chain containing LNs. The selection of the LN in the closest chain to the sink is as follows: Initially, the closest node to sink is elected LN in this latter because all nodes have initially the same energy value; then the future selection of the LN is based on the residual energy of the nodes. LNs in the other chains are selected based on the proximity to the previous LNs. Data transmission is performed in two steps: intra-chain transmission and inter-chain transmission. Furthermore, MCGA is simulated for different scenarios of mobility and density of nodes in the networks. The performance evaluation of the proposed technique shows a considerable reduction in terms of energy consumption and network lifespan.
The document discusses energy efficient routing protocols for clustered wireless sensor networks. It provides an overview of wireless sensor networks and discusses how clustering is commonly used to improve energy efficiency and scalability. The document reviews several existing clustering-based routing protocols and analyzes their approaches for prolonging network lifetime by minimizing energy consumption in wireless sensor networks.
Design and implementation of new routingIJCNCJournal
Energy consumption is a key element in the Wireless Sensor Networks (WSNs) design. Indeed, sensor nodes are really constrained by energy supply. Hence, how to improve the network lifetime is a crucial and challenging task. Several techniques are available at different levels of the OSI model to maximize the WSN lifetime and especially at the network layer which uses routing strategies to maintain the routes in the network and guarantee reliable communication. In this paper we intend to propose a new protocol called
Combined Energy and Distance Metrics Dynamic Routing Protocol (CEDM-DR). Our new approach considers not only the distance between wireless sensors but also the energy of node acting as a router in order to find the optimal path and achieve a dynamic and adaptive routing.
The performance metrics exploited for the evaluation of our protocol are average energy consumed, network lifetime and packets lost. By comparing our proposed routing strategy to protocol widely used in WSN namely Ad hoc On demand Distance Vector(AODV), simulation results show that CEDM-DR strategy might effectively balance the sensor power consumption and permits accordingly to enhance the network
lifetime. As well, this new protocol yields a noticeable energy saving compared to its counterpart.
Hierarchical Coordination for Data Gathering (HCDG) in Wireless Sensor NetworksCSCJournals
A wireless sensor network (WSN) consists of large number of sensor nodes where each node operates by a finite battery for sensing, computing, and performing wireless communication tasks. Energy aware routing and MAC protocols were proposed to prolong the lifetime of WSNs. MAC protocols reduce energy consumption by putting the nodes into sleep mode for a relatively longer period of time; thereby minimizing collisions and idle listening time. On the other hand, efficient energy aware routing is achieved by finding the best path from the sensor nodes to the Base Sta-tion (BS) where energy consumption is minimal. In almost all solutions there is always a tradeoff between power consumption and delay reduction. This paper presents an improved hierarchical coordination for data gathering (HCDG) routing schema for WSNs based on multi-level chains formation with data aggregation. Also, this paper provides an analytical model for energy consumption in WSN to compare the performance of our proposed HCDG schema with the near optimal energy reduction methodology, PEGASIS. Our results demonstrate that the proposed routing schema provides relatively lower energy consumption with minimum delay for large scale WSNs.
Data-Centric Routing Protocols in Wireless Sensor Network: A surveyAli Habeeb
This document summarizes several data-centric routing protocols for wireless sensor networks. It begins by outlining the challenges of routing in WSNs, including energy consumption, scalability, addressing, robustness, topology, and application-specific needs. It then describes several data-centric routing protocols, including flooding, directed flooding, constrained flooding, gossiping, fuzzy gossiping, location-based gossiping, and others. It notes advantages and disadvantages of these protocols for efficiently routing data in wireless sensor networks while minimizing energy consumption.
Multi Objective Salp Swarm based Energy Efficient Routing Protocol for Hetero...IJCNCJournal
Routing is a persistent concern in wireless sensor networks (WSNs), as getting data from sources to destinations can be a tricky task. Challenges include safeguarding the data being transferred, ensuring network longevity, and preserving energy in harsh environmental conditions. Consequently, this study delves into the suitability of using multi-objective swarm optimization to route heterogeneous WSNs in the hope of mitigating these issues while boosting the speed and accuracy of data transmission. In order to achieve better performance in terms of load balancing and reducing energy expenditure, the MOSSA-BA algorithm was developed. This algorithm combines the Multi-Objective Salp Swarm Algorithm (MOSSA) with the exploiting strategy of the artificial bee colony (BA) in the neighbourhood of Salps. Inspired by the SEP and EDEEC protocols, the integrated solutions of MOSSA-BA were used to route two and three levels of heterogeneous networks. The embedded solutions provided outstanding performance in regards to FND, HND, LND, percentage of remaining energy, and the number of packages delivered to the base station. Compared to SEP, EDEEC, and other competitors based on MOSSA and a modified multi-objective particle swarm optimization (MOPSO), the MOSSA-BA-based protocols demonstrated energy-saving percentages of more than 34% in medium-sized areas of interest and over 22% in large-sized areas of detection.
Multi Objective Salp Swarm based Energy Efficient Routing Protocol for Hetero...IJCNCJournal
The document proposes using multi-objective swarm optimization algorithms to route data in heterogeneous wireless sensor networks. Specifically, it develops the MOSSA-BA algorithm, which combines the Multi-Objective Salp Swarm Algorithm (MOSSA) with the artificial bee colony algorithm (BA). Testing shows the MOSSA-BA based routing protocols improve energy efficiency over 34% in medium areas and over 22% in large areas compared to SEP, EDEEC, and other competitors.
Design and Performance Analysis of Energy Aware Routing Protocol for Delay Se...ijcncs
This document presents a study on an energy aware routing protocol called Energy Aware DSR (EADSR) for wireless sensor networks. EADSR is an extension of the Dynamic Source Routing (DSR) protocol that adds energy awareness to improve network lifetime. The study compares the performance of DSR and EADSR through simulations. Results show that EADSR outperforms DSR in terms of energy savings and avoids early network partitioning caused by nodes draining their energy quickly. EADSR selects routes based on the total energy of nodes along the path and notifies neighbors when a node's energy is low to find alternative routes before it fails.
CFMS: A CLUSTER-BASED CONVERGECAST FRAMEWORK FOR DENSE MULTI-SINK WIRELESS SE...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
CFMS: A CLUSTER-BASED CONVERGECAST FRAMEWORK FOR DENSE MULTI-SINK WIRELESS SE...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
CFMS: A Cluster-based Convergecast Framework for Dense Multi-Sink Wireless Se...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
Similar to Grid Based Priority Routing Protocol for UWSNs (20)
Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post...IJCNCJournal
Recent natural disasters have inflicted tremendous damage on humanity, with their scale progressively increasing and leading to numerous casualties. Events such as earthquakes can trigger secondary disasters, such as tsunamis, further complicating the situation by destroying communication infrastructures. This destruction impedes the dissemination of information about secondary disasters and complicates post-disaster rescue efforts. Consequently, there is an urgent demand for technologies capable of substituting for these destroyed communication infrastructures. This paper proposes a technique for generating rendezvous sequences to swiftly reconnect communication infrastructures in post-disaster scenarios. We compare the time required for rendezvous using the proposed technique against existing methods and analyze the average time taken to establish links with the rendezvous technique, discussing its significance. This research presents a novel approach enabling rapid recovery of destroyed communication infrastructures in disaster environments through Cognitive Radio Network (CRN) technology, showcasing the potential to significantly improve disaster response and recovery efforts. The proposed method reduces the time for the rendezvous compared to existing methods, suggesting that it can enhance the efficiency of rescue operations in post-disaster scenarios and contribute to life-saving efforts.
Blockchain Enforced Attribute based Access Control with ZKP for Healthcare Se...IJCNCJournal
The relationship between doctors and patients is reinforced through the expanded communication channels provided by remote healthcare services, resulting in heightened patient satisfaction and loyalty. Nonetheless, the growth of these services is hampered by security and privacy challenges they confront. Additionally, patient electronic health records (EHR) information is dispersed across multiple hospitals in different formats, undermining data sovereignty. It allows any service to assert authority over their EHR, effectively controlling its usage. This paper proposes a blockchain enforced attribute-based access control in healthcare service. To enhance the privacy and data-sovereignty, the proposed system employs attribute-based access control, zero-knowledge proof (ZKP) and blockchain. The role of data within our system is pivotal in defining attributes. These attributes, in turn, form the fundamental basis for access control criteria. Blockchain is used to keep hospital information in public chain but EHR related data in private chain. Furthermore, EHR provides access control by using the attributed based cryptosystem before they are stored in the blockchain. Analysis shows that the proposed system provides data sovereignty with privacy provision based on the attributed based access control.
EECRPSID: Energy-Efficient Cluster-Based Routing Protocol with a Secure Intru...IJCNCJournal
A revolutionary idea that has gained significance in technology for Internet of Things (IoT) networks backed by WSNs is the " Energy-Efficient Cluster-Based Routing Protocol with a Secure Intrusion Detection" (EECRPSID). A WSN-powered IoT infrastructure's hardware foundation is hardware with autonomous sensing capabilities. The significant features of the proposed technology are intelligent environment sensing, independent data collection, and information transfer to connected devices. However, hardware flaws and issues with energy consumption may be to blame for device failures in WSN-assisted IoT networks. This can potentially obstruct the transfer of data. A reliable route significantly reduces data retransmissions, which reduces traffic and conserves energy. The sensor hardware is often widely dispersed by IoT networks that enable WSNs. Data duplication could occur if numerous sensor devices are used to monitor a location. Finding a solution to this issue by using clustering. Clustering lessens network traffic while retaining path dependability compared to the multipath technique. To relieve duplicate data in EECRPSID, we applied the clustering technique. The multipath strategy might make the provided protocol more dependable. Using the EECRPSID algorithm, will reduce the overall energy consumption, minimize the End-to-end delay to 0.14s, achieve a 99.8% Packet Delivery Ratio, and the network's lifespan will be increased. The NS2 simulator is used to run the whole set of simulations. The EECRPSID method has been implemented in NS2, and simulated results indicate that comparing the other three technologies improves the performance measures.
Analysis and Evolution of SHA-1 Algorithm - Analytical TechniqueIJCNCJournal
A 160-bit (20-byte) hash value, sometimes called a message digest, is generated using the SHA-1 (Secure Hash Algorithm 1) hash function in cryptography. This value is commonly represented as 40 hexadecimal digits. It is a Federal Information Processing Standard in the United States and was developed by the National Security Agency. Although it has been cryptographically cracked, the technique is still in widespread usage. In this work, we conduct a detailed and practical analysis of the SHA-1 algorithm's theoretical elements and show how they have been implemented through the use of several different hash configurations.
Optimizing CNN-BiGRU Performance: Mish Activation and Comparative AnalysisIJCNCJournal
Deep learning is currently extensively employed across a range of research domains. The continuous advancements in deep learning techniques contribute to solving intricate challenges. Activation functions (AF) are fundamental components within neural networks, enabling them to capture complex patterns and relationships in the data. By introducing non-linearities, AF empowers neural networks to model and adapt to the diverse and nuanced nature of real-world data, enhancing their ability to make accurate predictions across various tasks. In the context of intrusion detection, the Mish, a recent AF, was implemented in the CNN-BiGRU model, using three datasets: ASNM-TUN, ASNM-CDX, and HOGZILLA. The comparison with Rectified Linear Unit (ReLU), a widely used AF, revealed that Mish outperforms ReLU, showcasing superior performance across the evaluated datasets. This study illuminates the effectiveness of AF in elevating the performance of intrusion detection systems.
An Hybrid Framework OTFS-OFDM Based on Mobile Speed EstimationIJCNCJournal
The Future wireless communication systems face the challenging task of simultaneously providing high-quality service (QoS) and broadband data transmission, while also minimizing power consumption, latency, and system complexity. Although Orthogonal Frequency Division Multiplexing (OFDM) has been widely adopted in 4G and 5G systems, it struggles to cope with a significant delay and Doppler spread in high mobility scenarios. To address these challenges, a novel waveform named Orthogonal Time Frequency Space (OTFS). Designers aim to outperform OFDM by closely aligning signals with the channel behaviour. In this paper, we propose a switching strategy that empowers operators to select the most appropriate waveform based on an estimated speed of the mobile user. This strategy enables the base station to dynamically choose the waveform that best suits the mobile user’s speed. Additionally, we suggest retaining an Integrated Sensing and Communication (ISAC) radar approach for accurate Doppler estimation. This provides precise information to facilitate the waveform selection procedure. By leveraging the switching strategy and harnessing the Doppler estimation capabilities of an ISAC radar.Our proposed approach aims to enhance the performance of wireless communication systems in high mobility cases. Considering the complexity of waveform processing, we introduce an optimized hybrid system that combines OTFS and OFDM, resulting in reduced complexity while still retaining performance benefits.This hybrid system presents a promising solution for improving the performance of wireless communication systems in higher mobility.The simulation results validate the effectiveness of our approach, demonstrating its potential advantages for future wireless communication systems. The effectiveness of the proposed approach is validated by simulation results as it will be illustrated.
Enhanced Traffic Congestion Management with Fog Computing - A Simulation-Base...IJCNCJournal
Accurate latency computation is essential for the Internet of Things (IoT) since the connected devices generate a vast amount of data that is processed on cloud infrastructure. However, the cloud is not an optimal solution. To overcome this issue, fog computing is used to enable processing at the edge while still allowing communication with the cloud. Many applications rely on fog computing, including traffic management. In this paper, an Intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to address traffic congestion in heavily populated smart cities. The proposed system is implemented using fog computing and tested in a crowdedCairo city. The results obtained indicate that the execution time of the simulation is 4,538 seconds, and the delay in the application loop is 49.67 seconds. The paper addresses various issues, including CPU usage, heap memory usage, throughput, and the total average delay, which are essential for evaluating the performance of the ITCMS. Our system model is also compared with other models to assess its performance. A comparison is made using two parameters, namely throughput and the total average delay, between the ITCMS, IOV (Internet of Vehicle), and STL (Seasonal-Trend Decomposition Procedure based on LOESS). Consequently, the results confirm that the proposed system outperforms the others in terms of higher accuracy, lower latency, and improved traffic efficiency.
Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post...IJCNCJournal
Recent natural disasters have inflicted tremendous damage on humanity, with their scale progressively increasing and leading to numerous casualties. Events such as earthquakes can trigger secondary disasters, such as tsunamis, further complicating the situation by destroying communication infrastructures. This destruction impedes the dissemination of information about secondary disasters and complicates post-disaster rescue efforts. Consequently, there is an urgent demand for technologies capable of substituting for these destroyed communication infrastructures. This paper proposes a technique for generating rendezvous sequences to swiftly reconnect communication infrastructures in post-disaster scenarios. We compare the time required for rendezvous using the proposed technique against existing methods and analyze the average time taken to establish links with the rendezvous technique, discussing its significance. This research presents a novel approach enabling rapid recovery of destroyed communication infrastructures in disaster environments through Cognitive Radio Network (CRN) technology, showcasing the potential to significantly improve disaster response and recovery efforts. The proposed method reduces the time for the rendezvous compared to existing methods, suggesting that it can enhance the efficiency of rescue operations in post-disaster scenarios and contribute to life-saving efforts.
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.
May 2024, Volume 16, Number 3 - The International Journal of Computer Network...IJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
May_2024 Top 10 Read Articles in Computer Networks & Communications.pdfIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
A Topology Control Algorithm Taking into Account Energy and Quality of Transm...IJCNCJournal
The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.
Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy C...IJCNCJournal
The integration of artificial intelligence technology with a scalable Internet of Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. The multi-server environment within IoT introduces a flexible security service model, enabling users to interact with any server through a single registration. To ensure secure and privacy preservation services for resources, an authentication scheme is essential. Zhao et al. recently introduced a user authentication scheme for the multi-server environment, utilizing passwords and smart cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on Zhao et al.'s scheme, focusing on denial of service and privacy attacks, revealing a lack of user-friendliness. Subsequently, we propose a new multi-server user authentication scheme for privacy preservation with fuzzy commitment over the IoT environment, addressing the shortcomings of Zhao et al.'s scheme. Formal security verification of the proposed scheme is conducted using the ProVerif simulation tool. Through both formal and informal security analyses, we demonstrate that the proposed scheme is resilient against various known attacks and those identified in Zhao et al.'s scheme.
Advanced Privacy Scheme to Improve Road Safety in Smart Transportation SystemsIJCNCJournal
In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.
April 2024 - Top 10 Read Articles in Computer Networks & CommunicationsIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
High Performance NMF Based Intrusion Detection System for Big Data IOT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
1. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
DOI: 10.5121/ijcnc.2017.9601 1
GRID-BASED PRIORITY ROUTING PROTOCOL FOR
UWSNS
Faiza Al-Salti, N. Alzeidi, Khaled Day, Bassel Arafeh and Abderezak Touzene
Department of Computer Science, Sultan Qaboos University, Oman
ABSTRACT
In this paper, we devise and evaluate a new Grid-Based Priority Routing (GBPR) protocol for Underwater
Wireless Sensor Networks (UWSNs). GBPR utilizes a 3D logical grid view of the monitored area to deliver
data packets to sink nodes. Particularly, data packets are forwarded on a cell-by-cell-basis using elected
sensor nodes called cell-heads. The unique feature in GBPR is the classification of the neighboring cells in
different priority levels according to their distances to the sink node. Cells closer to the sink are given
higher priority to be selected as the next hop. This mechanism helps in reducing the number of hops; thus,
reducing the energy consumption and end-to-end delay, and increasing the reliability. The protocol is
evaluated and compared against EMGGR and EEF protocols available in the literature. Simulation results
show that GBPR outperforms the other two protocols in terms of energy efficiency, average delay and
packet delivery ratio.
KEYWORDS
Underwater wireless sensor networks, UWSNs, routing protocol, location-based, grid, cell-head election&
void-handling algorithm.
1. INTRODUCTION
Underwater wireless sensor networks (UWSNs) consist of a set of sensor nodes deployed in the
underwater environment. These nodes are capable of sensing, detecting, tracking and reporting
data about the monitored environment to specific nodes called sinks, which are located at the
water surface. They can measure a variety of features such as temperature, salinity and pressure;
thus, enable several applications such as environmental monitoring, undersea exploration, disaster
prevention, and assisted navigation [1]. However, UWSNs experience a number of challenges
induced by the nature of the environment and the used underwater acoustic communication.
Particularly, the speed of acoustic signals underwater is five orders of magnitude slower than the
speed of radio signals in terrestrial networks. In addition, underwater channels are severely
weakened due to the multi-path and fading effects. Moreover, underwater sensor nodes are
equipped with batteries of limited energy, and they are difficult to be replaced or recharged.
Besides, the available bandwidth is limited and inversely proportional to the transmission
distance. Furthermore, the three-dimensional (3D) deployment and the dynamic environment due
to the passive movement of sensors with water currents cause extra challenges in the development
of new schemes (e.g. routing and localization) for such UWSNs.
Several solutions have been proposed to solve different issues in UWSNs such as node’s
deployment, node’s mobility, MAC, routing, and localization. Nodes’ deployment, for example,
is an important task in UWSNs because several network services like routing protocols,
localization schemes and network topology control are built on top of it [2]. Generally, the
objectives of node deployment schemes in UWSNs are achieving high network connectivity, high
coverage, less number of nodes, low energy consumption and high delivery ratio [2]. G. Han et al.
[3] have surveyed and classified deployment strategies in UWSNs. The classification is based on
three categories namely; static deployment, self-adjustment and movement-assisted deployment
schemes.
2. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
2
Several MAC protocols have been proposed for UWSN such as those found in [4], [5], [6], [7],
and [8].According to [9], MAC protocols for UWSNs can be classified into three categories:
contention-based, contention-free and hybrid MAC protocols.
Because sensor nodes might be deployed at farther distances from the sink nodes, multi-hop
routing is necessary to manage the communications between sensors and sink nodes with efficient
use of the available resources (i.e. bandwidth, power and memory storage). General objectives of
the proposed routing protocols are high energy-efficiency, low average delay and high packet
delivery ratio. Though, these protocols face some issues like high energy-consumption in dense
networks, low packet delivery ratio in sparse networks and generally high delay. Therefore, in
this paper, we present a Grid-Based Priority Routing (GBPR) protocol for UWSNs that aims to
mitigate the earlier mentioned challenges, and to achieve high energy-conservation, low average
end-to-end delay and high packet delivery ratio under different network conditions.
GBPR utilizes a 3D grid view to deliver data packets from cell to cell via special nodes called
cell-heads that are elected using an election algorithm. The election algorithm, as described in
section 3, uses short control packets in order to reduce the network overhead. The main feature of
the proposed protocol that differentiates it from the existing grid-based protocols is the
classification of the neighboring cells into different priority levels according to their distances to
the sink node. This classification facilitates the selection of the packets’ forwarders. Basically, in
each hop, neighboring cells that are closer to the destination cell than the current cell are favored
to forward data packets.
The rest of this article is organized as follows. In section 2, different classifications of existing
routing protocols in UWSNs along with some examples are discussed. Section 3 describes the
proposed routing protocol. Evaluation and comparison results of the protocol are provided in
section 4. Finally, the paper is concluded in section 5.
2. RELATED WORK
Several routing protocols have been proposed specifically for UWSNs. These protocols can be
classified based on different criteria. For example, VBF [10], HH-VBF [11], VBVA [12], LE-
VBF [13] and DFR [14] are typical location and flooding based routing protocols that aim to
direct packets’ propagation and reduce the number of forwarders as compared to uncontrolled
flooding protocols. Thus, they reduce the number of collisions and improve the energy
consumption.
Depth-based routing protocols rely mainly on depth information for selecting candidate
forwarders. Depth information can be determined using inexpensive depth sensors [15]. Example
of such protocols is the EEF [16] routing protocol, which is developed to achieve energy
efficiency. Depth information along with residual energy and the distances to the sink node and to
the previous forwarder are used to calculate the fitness value. This value is used to determine the
best eligible forwarders. Each possible candidate should hold the packet for a certain time based
on its depth, distances and energy before forwarding it. If the period expires without overhearing
the transmission of that packet, it broadcasts the packet. Despite its simplicity, it faces void
problem in highly sparse networks and severe collisions between packets in dense networks.
Grid-based geographic routing protocols for UWSNs have been proposed in [17], [18] and [19].
They all aim to improve the energy efficiency of the networks. Mainly, MGGR [17] and EMGGR
[18] are based on gateways (i.e. elected sensor nodes) to forward data packets on a cell-by-cell
basis. They route packets over disjoint paths constructed based on the grid view rather than the
position of the nodes in order to reduce the need for paths’ maintenance due to topology changes.
However, low packet delivery ratio is achieved and high average delay is incurred in sparse
networks.
3. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
3
The GFGD and GGFGD [19] are two other grid-based protocols for UWSNs. They incorporate a
duty cycle (i.e. some nodes are scheduled to sleep for some time) mechanism, which can balance
and save energy. In addition, they use path delay, path loss and remaining energy for relay
selection. However, the protocols assume that the channel link is symmetric, which is not
practical in underwater since acoustic channels in underwater are known to be asymmetric
[10][20].
Although several routing protocols have been proposed for UWSNs, they are facing general
issues: (i) high energy consumption in dense networks due to the overhead incurred by
broadcasting a massive number of packets, (ii) low packet delivery ratio in sparse networks, (iii)
high end-to-end delay, and (iv) low performance with node mobility. Our proposed solution in
this paper strives to solve these issues as will be described and proved in the following sections.
3. THE PROPOSED ROUTING PROTOCOL
GBPR is a location-based routing protocol that advances a routed packet, in each hop, towards the
sink nodes at the surface level. It is based on viewing the network as a 3D logical grid and the
forwarding is performed in a cell-by-cell manner. Additionally, a specific set of sensor nodes,
called cell-heads, are the only nodes eligible for data forwarding at any point in time. Since
applications may employ more than one link to receive data packets, the closest link to the current
forwarder is selected as a destination target. The proposed protocol also adapts a void handling
technique when a void cell (i.e. a cell with no elected node) is encountered. The proposed
mechanisms are expected to achieve high energy-efficiency, low average delay and high delivery
ratio.
3.1 SYSTEM MODEL
3.1.1 ASSUMPTIONS
In this paper, we consider a 3D underwater wireless sensor network in which sensor nodes are
distributed in a 3D area and the sink nodes are positioned at the surface level (see Figure 1). The
following is assumed:
• The network consists of a set N = Ns∪Nnof sink and sensor nodes, where Ns is the set of
sink nodes and Nn is the set of sensor nodes. All nodes including the sinks are of equal
acoustic communication range R. The sink nodes in Ns are assumed to be stationary. In
addition, they use acoustic channels to communicate with the underwater sensor nodes and
radio channels to communicate with each other and with terrestrial stations.
• All data packets are forwarded to the sink nodes.
• Because of the rapid speed of radio propagation in the air compared to acoustic propagation
under water, data packets received by any sink are assumed to be received by other sinks in
a negligible time.
• The set Nnof the sensor nodes are assumed to have similar capabilities (e.g. transmission
range, storage space and initial energy level).
• Each sensor node i is assumed to be able to obtain its physical location information in the
Cartesian coordinates (Xi, Yi, Zi) from an existing localization service. It is also assumed to
know the location of the sink nodes.
3.1.2 CONSTRUCTION OF THE LOGICAL GRID
The geographic region is divided into 3D logical grids as shown in Figure 2. Cells are viewed as
cubes of equal volume d3
, where d is the length of the cell side. The number of cells along ani-
axis (where i is one of the three dimensions x, y, z) is known as Ki (e.g. Kx is the number of cells
4. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
along the x-axis), which can vary from one axis to another.
origin, which is assumed at the top left of the surface,
The node uses its location information,
d to determine the XYZ coordinates of its cell (x
physical location of a node i is referred to by the upper case (X
of the cell in which it is hosted are
unique ID (NID) and a cell ID (CID
belongs to, which can be determine
, ,
The transmission range R of the sensors is used to
in a cell can communicate directly
common vertex, edge or face with it
common face with the original cell) as shown i
communicating nodes are located at the farthest diagonally apart corners of the neighboring
cubes. As illustrated in Figure 4,
The 32 neighboring cells are: (x
1,y,z+1), (x-1,y+1,z-1), (x-1,y+1,z), (x
(x,y,z+1), (x,y+1,z-1), (x,y+1,z), (x,y+1,z+1), (x+1,y
1), (x+1,y,z), (x+1,y,z+1), (x+1,y+1,z
2,z), (x,y+2,z), (x,y,z-2), (x,y,z+2).
It is worth noting that a cell can communicate partially with other cell
neighbors; however, for the sake of simplicity
is used to check whether a given cell is one of the 32 neighboring cells or not.
Figure 1: 3D UWSN architecture
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
axis), which can vary from one axis to another. The physical location of the
, which is assumed at the top left of the surface, is denoted by (X0, Y0, Z0).
The node uses its location information, the location of the grid origin (X0, Y0, Z0) and the value of
to determine the XYZ coordinates of its cell (xi, yi, zi) as given in (1). Note that the
is referred to by the upper case (Xi, Yi, Zi), while the grid coordinates
he cell in which it is hosted are denoted by the lower case (xi, yi, zi). Every sensor node has a
CID). CID is a number that identifies the cell that the sensor node
belongs to, which can be determined from the XYZ coordinate of the cell (2).
, ,
∗ ∗
The transmission range R of the sensors is used to compute the value of cell side, such that a node
in a cell can communicate directly with any node in its 32 neighboring cells (i.e. cells that have a
with it; or cells that have a common face with those having a
common face with the original cell) as shown in Figure 3. This can be achieved when the
communicating nodes are located at the farthest diagonally apart corners of the neighboring
cubes. As illustrated in Figure 4, d should be selected to satisfy 2 √3.
The 32 neighboring cells are: (x-1,y-1,z-1), (x-1,y-1,z), (x-1,y-1,z+1), (x-1,y,z-1), (x
1,y+1,z), (x-1,y+1,z+1), (x,y-1,z-1), (x,y-1,z), (x,y-1,z+1), (x,y,z
1), (x,y+1,z), (x,y+1,z+1), (x+1,y-1,z-1), (x+1,y-1,z), (x+1,y-1,z+1), (x+1,y,z
1), (x+1,y,z), (x+1,y,z+1), (x+1,y+1,z-1), (x+1,y+1,z), (x+1,y+1,z+1), (x-2,y,z), (x+2,y,z), (x,y
2), (x,y,z+2).
It is worth noting that a cell can communicate partially with other cells apart from the
neighbors; however, for the sake of simplicity, these are not considered as neighbors.
is used to check whether a given cell is one of the 32 neighboring cells or not.
WSN architecture Figure 2: A view of 3D logical grid
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
4
The physical location of the grid
) and the value of
Note that the absolute
), while the grid coordinates
Every sensor node has a
). CID is a number that identifies the cell that the sensor node
1
2
, such that a node
with any node in its 32 neighboring cells (i.e. cells that have a
a common face with those having a
n Figure 3. This can be achieved when the
communicating nodes are located at the farthest diagonally apart corners of the neighboring
1), (x-1,y,z), (x-
1,z+1), (x,y,z-1),
1,z+1), (x+1,y,z-
2,y,z), (x+2,y,z), (x,y-
s apart from these 32
these are not considered as neighbors. Algorithm 1
: A view of 3D logical grid
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Figure 3: A cross section of the 32 neighboring cells
Procedure: isNeighbor
//for checking if cell (Nx, Ny, Nz) is a neighbor of current cell (Cx, Cy, Cz)
x=|Cx- Nx|
y= |Cy- Ny|
z= |Cz- Nz|
if((x=0) & (y=0) & (z=0))
return false; //local cell
else if (((x≤1) & (y≤1) &
(y=2) & (z=0)) Or ((x=0) & (y=0) & (z=2)))
return true; //neighbor cell
else
return false; //neither local nor neighbor
Algorithm 1: Procedure for checking
3.2 NOTATIONS
We use the notations defined in Table 1 to describe the proposed
Table 1: Notations used in the protocol’s description
Symbol Definition
D The length of the cell side
R Node’s transmission range
Kx, Ky, Kz Number of cells along the x
Cell-head A node elected to forward data packets from the corresponding cell
Head-table A table used to store the cell
NID Unique node identifier
CID Unique cell identifier
Ε The minimum amount of energy to continue serving as a cell
Head-packet A packet sent by a node upon electing itself as a cell
Update-packet A periodic packet sent by a cell
existence
Retire-packet A packet sent by a cell
threshold
Exit-packet A packet sent by a cell head upon roaming
Update-timer A timer set by each cell
its existence by sending Update
Election-timer A timer set by non
Local cell The cell in which the node resides in
Neighbor cell A cell defined
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
ion of the 32 neighboring cells Figure 4: Finding the value of the cell side
Procedure: isNeighbor (Cx, Cy, Cz, Nx, Ny, Nz)
//for checking if cell (Nx, Ny, Nz) is a neighbor of current cell (Cx, Cy, Cz)
((x=0) & (y=0) & (z=0))
; //local cell
1) & (y≤1) & (z≤1)) Or ((x=2) & (y=0) & (z=0)) Or ((x=0) &
(y=2) & (z=0)) Or ((x=0) & (y=0) & (z=2)))
; //neighbor cell
; //neither local nor neighbor
Algorithm 1: Procedure for checking a neighboring cell
We use the notations defined in Table 1 to describe the proposed GBPR protocol.
: Notations used in the protocol’s description
Definition
The length of the cell side
transmission range
Number of cells along the x-axis, y-axis and z-axis respectively
A node elected to forward data packets from the corresponding cell
A table used to store the cell-heads in the neighboring cells
Unique node identifier
Unique cell identifier
The minimum amount of energy to continue serving as a cell-
A packet sent by a node upon electing itself as a cell-head
A periodic packet sent by a cell-head to inform neighbors about its
existence
A packet sent by a cell-head when its remaining energy falls below
threshold ε
A packet sent by a cell head upon roaming out of its cell
A timer set by each cell-head to periodically inform neighbors about
its existence by sending Update-packet
A timer set by non-cell head nodes to start an election process
The cell in which the node resides in
defined according to Algorithm 1 andillustrated in Figure
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5
Figure 4: Finding the value of the cell side
//for checking if cell (Nx, Ny, Nz) is a neighbor of current cell (Cx, Cy, Cz)
1)) Or ((x=2) & (y=0) & (z=0)) Or ((x=0) &
axis respectively
A node elected to forward data packets from the corresponding cell
-head
neighbors about its
remaining energy falls below the
periodically inform neighbors about
election process
illustrated in Figure 3
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3.3 DETERMINING THE NEAREST SINK CELL
Recall that there might be more than one sink node deployed for receiving data packets. In that
case, each node should construct its head-table relative to the nearest sink. A sensor node
determinesits nearest sink based on the Euclidean distances between the cell containing the sensor
node and the cells containing the sink nodes.
3.4 CLASSIFICATION OF THE NEIGHBORING CELLS
Remember that a node in a cell i can have up to 32 neighboring cells; yet, these cells differ in
their distances to the sink cell (the nearest sink). In other words, some of the cells are closer to the
sink than the cell i and some are farther from the sink. However, to achieve low average end-to-
end delay and to save the overall energy, cells that are closer to the sink cell are given higher
priority to be selected for packet relay. Thus, neighboring cells are ordered in different priority
levels according to their distances to the sink cell. To explain these levels, assume that the source
cell i, the sink cell s, the neighboring cell to be classified n have the coordinates (xi,yi,zi), (xs,ys,0)
and (xn,yn,zn), respectively. Let δmjk = |mj-mk| (where m is either x, y or z) be the distance along
the m-axis between cell j and cell k.
• Vertical positive level (group G1): This set G1 of neighboring cells includes those cells that
are closer to the sink cell along the z dimension, and closer or equal to the sink cell along
each of the x and y-dimensions. In other words, if the δzns is less than δzis, and δxns andδyns
is less than or equalto δxis and δyis, respectively, then the neighbor cell is classified as a
vertical positive neighbor cell and placed in this group G1.
• Horizontal positive level (group G2): This set G2 of neighboring cells includes those cells
that are closer to the sink cell along the x dimension or y dimension, and the remaining are
equal to that of the source cell. In othero, if one or both of δxns and δyns is, respectively less
than δxis and δyis, and the remaining of δxns, δyns and δznsare equal to the corresponding δxis,
δyis and δzis, then the neighbor cell falls in this group G2.
• Positive and negative level (group G3): This set of cells includes those cells that are closer
to the sink cells along one or more dimensions. However, it is farther to the sink than the
current cells along the other dimensions. That is if any of δxns, δyns and δzns is less than δxis,
δyis and δzis, respectively and the remaining are greater than the corresponding δxis, δyis and
δzis, then the neighbor cell falls in this group G3.
• Negative level (group G4): This set of cells includes those cells that are farther from the
sink cells along one or more dimensions while the other dimensions remain equal to that of
the current cell. In other words, if any of δxns, δyns and δzns is greater than δxis, δyis and δzis,
respectively, and the remaining are equal to the corresponding δxis, δyis and δzis, then the
neighbor cell is placed in this group G4.
Algorithm 2 further defines these levels. The head-table of each cell-head is divided into four
disjoint sub-tables according to the above priority levels. Figure 5 shows a general structure of the
head-table. It is worth mentioning that the classification of neighboring cells into the priority
levels takes place while constructing the head-tables. Particularly, when a cell-head receives a
head-packet or an update-packet (see section 3.5) from one of the neighboring cells, it checks the
priority level of that cell and accordingly stores the information in the relevant sub-table.
When a node enters a new cell, the priority levels of the neighboring cells might change. In
addition, some of the cells may no longer be neighbor cells and need to be removed from the
head-table. Furthermore, new cells are becoming neighboring cells for that node. Thus, when the
node moves out from its cell, it updates its head-table based on the new cell it enters.
7. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
Figure 6 illustrates these priority levels for
grid. To further illustrate this, assume that the source node is in the cell
is in the cell D (5,6). Figure 6 shows 12 neighboring cells for
with xy-coordinates (3,2), it is clear that t
dimension. However, the y-dimension is same as that for
in the second priority group (G2
is closer to the sink in the x-dimension, but farther from the sink than
Therefore, this cell is placed in the third priority group (
neighboring cells are shown inside each cell into the three priority le
cell in G1 in the figure since the source and sink cells are in the same z
Figure 6: Demonstration of the priority levels in a 2D grid
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
Figure 6 illustrates these priority levels for the 2D grid. The same idea can be extended
grid. To further illustrate this, assume that the source node is in the cell S (2,2) and the sink node
shows 12 neighboring cells for S. Considering the neighboring cell
coordinates (3,2), it is clear that the cell is closer to the sink cell than S
dimension is same as that for S. Thus the cell-head in S
G2). Another example is the cell with xy-coordinate (1,3). This cell
dimension, but farther from the sink than S in the y
Therefore, this cell is placed in the third priority group (G3). The priority levels of the 12
neighboring cells are shown inside each cell into the three priority levels. Note that there is no
cell in G1 in the figure since the source and sink cells are in the same z-level.
Figure 5: The structure of the head-table
: Demonstration of the priority levels in a 2D grid
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7
can be extended to the 3D
(2,2) and the sink node
. Considering the neighboring cell
S along the x-
S puts this cell
coordinate (1,3). This cell
in the y-dimension.
). The priority levels of the 12
vels. Note that there is no
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8
Procedure: classifyCell(nx, ny, nz, ix, iy, iz, sx, sy, sz)
//(nx,ny,nz) => xyz-coordinates of the neighbor cell
//(sx,sy,sz) => xyz-coordinates of the sink cell
//(ix,iy,iz) => xyz-coordinates of the current cell
δxis =|ix-sx|
δyis =|iy-sy|
δzis =|iz-sz|
δxns =|nx-sx|
δyns =|ny-sy|
δzns =|nz-sz|
if( (δxns ≤ δxis) && (δyns ≤ δyis) && (δzns<δzis))
returnG1; //vertical positive level
else if( (δxns ≤ δxis) && (δyns ≤ δyis) && (δzns==δzis))
returnG2; //horizontal positive level
else if( (δxns ≥ δxis) && (δyns ≥ δyis) && (δzns ≥ δzis))
return G4; //negative level
else
returnG3; //positive and negative level
Algorithm 2: Procedure for classifying a neighboring cell
3.5 CELL-HEAD ELECTION
We present in this section a cell-head election algorithm for electing cell-heads, which will be
responsible for forwarding data packets. Using cell-heads has the objective of reducing the
number of data packets propagated in the network. Particularly, each cell-head is responsible for
forwarding packets from its cell to a neighboring cell. In order to reduce the overhead that can be
incurred by the election algorithm, the proposed election algorithm uses only few and small
control packets. The adopted algorithm is based mainly on the residual energy of the nodes.
Basically, in a given cell each node with residual energy above a predefined threshold ε can
compete for being the head of the cell. Each node, including non-heads, records the cell-heads in
the local and neighboring cells in a table called head-table, as defined earlier. The election
algorithm consists of two phases; an initialization phase and a maintenance phase as described
below.
3.5.1.INITIALIZATION PHASE
• After deployment, sensor nodes in each cell select a random time to initiate an election
process. Each node broadcasts a head-packet <type, CID, NID>, where type indicates the
type of the packet (head-packet in this case), CID is the cell ID that the node belongs to, and
NID is the ID of the node sending the packet. In addition, the node records itself as the head
of that cell, and sets an update-timer to send an update-packet upon timer expiration.
• Each node, upon receiving a head-packet from a local or a neighboring cell, stores the head
information in its head-table. Furthermore, local nodes stop competing for being the heads
for the current period. However, they set election-timers to compete for cell-head in the next
period. If more than one head-packet is received from the same cell (due, for example, to
two nodes initiating the election process at nearly the same time), then the node
corresponding to the last received head-packet is assumed the cell-head.
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3.5.2.MAINTENANCE PHASE
• When the update-timer of a cell-head expires, it checks its remaining energy. If it is above
the threshold ε, the node broadcasts an update-packet <type, CID, NID>, and continues
serving as a cell-head for the next period. Local and neighboring nodes update their head-
tables accordingly. Otherwise, if its energy is below the threshold, it broadcasts a retire-
packet<type, CID, NID> to inform local nodes to start a new election.
• Local and neighboring nodes upon receiving a retire-packet remove the information related
to that node from their head-tables. In addition, local nodes with energy levels above the
threshold ε start a new election process as given in the initialization phase.
• When a head node exits from its cell, it broadcasts an exit-packet <type, CID, NID>. Upon
receiving that packet, local and neighboring nodes remove the information related to the
source node of that packet from their head-tables. Also, local nodes start a new election
process as described in the initialization phase.
• When an election-timer of a node expires, the node sets itself as a cell-head and broadcasts
a head-packet. Local and neighboring nodes when receiving that packet set that node as the
cell-head. In addition, the head in the local cell, if any, cancels its update-timer.
3.6 RELAY SELECTION AND PACKET FORWARDING
Each data packet consists of current NID, next NID, crossed-cells and payload. Current NID is
the ID of the current forwarder. Next NID is the ID of the forwarder in the next hop, crossed-cells
is a sequence of CIDs of the cells that the packet already crossed, and the payload is the
information to be delivered to the sink nodes. Figure 7 demonstrates a general format of the data
packet.
When a node has a data packet to be forwarded, it looks for a cell-head in the first priority group
in a round robin fashion. It selects a cell from that group given that it has a cell-head. Once the
cell-head is selected, the node updates the packet header by setting current NID to its ID and next
NID to the ID of the selected node, and appending its CID to the end of the crossed-cell. Then, it
sends the packet to the selected node. This forwarding process continues until the packet is
delivered to the sink node. If the first group is empty (i.e. does not contain any cell-head), it
searches for a cell-head in the second group in a similar way (i.e. round robin). If there is no cell-
head in the second group, it looks for a cell-head in the third group and then in the fourth group.
Once the cell-head is selected, the node checks that the selected node is not hosted in a cell
contained in the crossed-cell of the packet. If it is the case, then the node updates the packet
header by setting current NID to its ID and next NID to the ID of the selected node and appending
its CID to the end of the crossed-cell. Then, it sends the packet to the selected node. Otherwise,
the node looks for another node to be selected. This check is used to ensure a loop-free
forwarding. The process continues until the packet is delivered to the sink node.
If at any hop, the forwarder could not find any cell-head in all its neighboring cells (i.e.
throughout the paper, this node is called a void node and its cell as a void cell), it sends a negative
acknowledgment to the previous forwarder. The previous forwarder, in turn, looks for another
cell (other than that cell) containing a node to forward the packet to it, in the same way, explained
earlier.
Current
NID
Next
NID
crossed-cells payload
Figure 7: Format of the data packet
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4. SIMULATION RESULTS
4.1 SIMULATION SETTINGS
In this section, we evaluate the performance of our proposed routing protocol against EEF and
EMGGR routing protocols (summarized in section 2) via Aqua-Sim [21] simulator. Aqua-Sim is
a simulation package developed specifically for UWSNs, and it is based on NS2 simulator.
Sensor nodes are deployed in a 3D area of size (3x3x3) km3
. Sink nodes are located at the surface
level and are assumed to be stationary. The brodcastMac protocol [10] is used in in all scenarios.
The idea of this MAC protocol is as follows: when a node has a packet to be transmitted, it senses
the channel and broadcasts the packet if the channel is idle; otherwise, it backs off. If the number
of back-off times exceeds a specified limit, the packet will be dropped. The back-off limit used in
all simulations in this research is four, which is the default value used in the simulator. Upon
receiving the packet, the receiver does not need to acknowledge the reception of the packet.
The capabilities of the sensor nodes are set as follows. The transmission range is set to 1.5 km.
The transmission, reception and idle powers are set to 8.0 W, 0.80 W and 0.008 W, respectively.
The frequency is set to 35.695 kHz, and 17.80 kbps is used for the bit rate. The bit error rate is set
to 10e-9
. This setup is similar to the specification of the real underwater acoustic sensor modem
UWM2000H[22].
The simulation type adopted in this evaluation is the terminating state where each run lasts for
2000 seconds. Results from the first 150 seconds and the last 150 seconds are discarded to
minimize the warm-up effect. In each run, 10 sensor nodes are selected randomly from the set of
deployed sensors to inject exponentially distributed traffic into the network. Sensor nodes
mobility, if not otherwise specified, is assumed to follow the 2D random walk [23][24] mobility
model which is one of the most widely used mobility model [23]. Table 2 summarizes other
system parameters.
Table 2: Additional system parameters
Parameter Value
Number of nodes 200, 300, 400
Number of sinks 1, 2, 3
Initial energy 300 J
Data packet size 150 Byte
Energy threshold 20J
Alpha for EMGGR 0.5
4.2 INVESTIGATED METRICS
The performance of the proposed routing protocol is studied and compared with the performance
of existing solutions by investigating the following performance metrics [25][18]:
• Packet Delivery Ratio (PDR): PDR is the ratio of the number of distinct data packets
delivered successfully to the sink nodes to the total number of data packets generated at the
source nodes.
• Average End-To-End Delay: The average time taken by a data packet to arrive to the
destination. It is computed from the time the packet is generated until it reaches the
destination. Only data packets that were successfully delivered to the sink nodes are
counted.
• Energy Consumption: The total energy consumed by all nodes during the simulation. It
includes the power consumed in the transmission, reception and idle modes.
11. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
11
• It is worth noting that the mentioned metrics are investigated under the effect of varying the
number of sensor nodes, traffic load and node’s mobility as follows:
• Number Of Nodes: The effect of the number of sensor nodes is investigated and assessed.
Basically, 200, 300 and 400 stationary nodes are deployed in the investigated environment.
The traffic injection rate is set to 0.07 packets/s.
• Traffic Load: The impact of traffic load is assessed by varying the packet injection rate
from 0.05 to 0.11 packets/s. In this set of experiments, 300 static nodes are deployed in the
environment.
• Node’s Mobility: According to [26], underwater objects can move at speed 3-6 km/h.
Therefore, the effect of nodes’ mobility is evaluated by varying the maximum speed of
nodes from 0 to 1.5 m/s. The minimum speed is set to 0 m/s. The number of nodes used in
this set of experiments is 300 and the traffic injection rate is set to 0.07 packets/s.
4.3 SIMULATION RESULTS
A large number of experiments have been conducted to evaluate the performance of the proposed
protocol. In the first subsection, the impacts of the number of sensor nodes, traffic rate and speed
of nodes of the three protocols are assessed under random deployment. In this set of experiments,
a single sink is used, and it is deployed at the middle of the top surface level. The second
subsection evaluates the protocol using multiple sinks. 95% confidence intervals were calculated
for all collected results. The average of 20 batch runs along with error bars are presented in the
figures.
4.3.1 GBPR VERSES EEF AND EMGGR
Impact Of The Number Of Sensor Nodes:
Figure 8.a shows the impact of the number of nodes on the total energy consumed by the nodes.
The figure reveals that the three protocols show an increase in the total consumed energy as the
number of nodes increases. This can be justified by the increase in the number of packets
propagated in the network and the number of nodes participating in the transmission and
reception of these packets. However, the increase is very sharp in the EEF protocol since each
forwarder broadcasts the packet to all its neighbors, and those with higher fitness than the
previous hop are possible eligible forwarders. Although, in GBPR and EMGGR, data packets are
forwarded hop-by-hop through cell-heads only, all nodes in the range may receive the packets due
to the shared transmission media. The energy consumption in EMGGR is a little bit higher than
GBPR due to the long paths a high number of control packets. Specifically, when the number of
nodes is 300, GBPR is better than EEF and EMGGR in saving energy by 70% and 28%
respectively.
Figure 8.b demonstrates the effect of the number of sensor nodes on the average end-to-end delay.
As revealed in the figure, the average delay of both EMGGR and EEF exhibits a similar trend as
the number of nodes increases. EEF shows the highest end-to-end delay. This is due to the fact
each node holds the packet for a certain time based on its depth, residual energy, and its distance
from the previous forwarder. In addition, since each relay broadcasts the packet to all its
neighbors, there will be a high workload and congestion in the channel, which increases the
queuing time and results in a high end-to-end delay. In contrast, GBPR and EMGGR do not
employ any waiting time because only a single forwarder is selected in each hop. In GBPR, the
increase in the number of nodes minimizes the number of voids; hence, increasing the probability
of finding forwarders that give positive progress towards the sink nodes. As a result, packets are
propagated in less number of hops. This is the justification for the decrease in the average end-to-
end delay in GBPR. With 300 nodes, GBPR is better in the average end-to-end delay than EEF
and EMGGR by 48% and 26%, respectively.
12. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
Figure 8.c illustrates the packet delivery ratio (PDR) as a function of the number of nodes. By
increasing the number of nodes, both
the PDR. The improvement can be justified by the mechanisms
to (i) reduce the number of packets propagated in the network by selecting a single forwarder in
each hop, and (ii) distribute the load among the nodes by
forwarders. These indeed reduce
encountering void cells (i.e. cells with no cell
nodes; hence, packets have better chances to be forwarded
shorter paths. On the contrary, as the number of nodes increases in EEF, less delivery ratio is
achieved due to the increase in the number of candidate forwarders (i.e. nodes with high fitness
than the previous forwarders) as the number of nodes increases. Hence, packets collide with each
other and fail to be delivered successfully to the destinations. It is worth mentioning that
outperforms both EEF and EMMGR over
number of nodes is set to 300,
respectively.
(a)
(a) Energy consumption (b)
Figure 8: Impact of the number of sensor nodes on
0
10
20
30
40
50
60
70
80
90
100
110
200 300
Energyconsumption(KJ)
Number of nodes
Energy consumption
GBPR EMGGR
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
PDR(pkts/s)
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
Figure 8.c illustrates the packet delivery ratio (PDR) as a function of the number of nodes. By
increasing the number of nodes, both GBPR and EMGGR protocols provide an improvement in
improvement can be justified by the mechanisms adopted by the protocols that try
reduce the number of packets propagated in the network by selecting a single forwarder in
(ii) distribute the load among the nodes by alternating the selection of the
reduce the number of collisions. Moreover, the probability of
encountering void cells (i.e. cells with no cell-heads) decreases by increasing the number of
nodes; hence, packets have better chances to be forwarded successfully to their next hops on
shorter paths. On the contrary, as the number of nodes increases in EEF, less delivery ratio is
achieved due to the increase in the number of candidate forwarders (i.e. nodes with high fitness
) as the number of nodes increases. Hence, packets collide with each
other and fail to be delivered successfully to the destinations. It is worth mentioning that
erforms both EEF and EMMGR overall used number of nodes. For example, when the
of nodes is set to 300, GBPR is, better than EEF and EMGGR by 13% and 31%,
(b)
(c)
nergy consumption (b) Average end-to-end delay (c) PDR
Figure 8: Impact of the number of sensor nodes on
400Number of nodes
Energy consumption
EMGGR EEF
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
200 300 400
Delay(s)
Number of nodes
Average end-to-end delay
GBPR EMGGR EEF
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
200 300 400
Number of nodes
Packet Delivert Ratio (PDR)
GBPR EMGGR EEF
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
12
Figure 8.c illustrates the packet delivery ratio (PDR) as a function of the number of nodes. By
and EMGGR protocols provide an improvement in
by the protocols that try
reduce the number of packets propagated in the network by selecting a single forwarder in
selection of the
Moreover, the probability of
heads) decreases by increasing the number of
successfully to their next hops on
shorter paths. On the contrary, as the number of nodes increases in EEF, less delivery ratio is
achieved due to the increase in the number of candidate forwarders (i.e. nodes with high fitness
) as the number of nodes increases. Hence, packets collide with each
other and fail to be delivered successfully to the destinations. It is worth mentioning that GBPR
all used number of nodes. For example, when the
is, better than EEF and EMGGR by 13% and 31%,
400
end delay
EEF
13. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
Impact Of Traffic Rate:
As revealed in Figure 9.a, the energy consumption is positively proportional to the inj
of the traffic overall evaluated protocols. However,
three protocols. The key causes of the energy efficiency of
packets employed by GBPR, the selection of a single forwarding candidate in each hop, and the
selection of the closest feasible neighboring cell to the destination cell
energy consumption between GBPR
EMGGR incurs only a small increase in the energy consumption compared to
expected since both of them select a single candidate forwarder in each hop, howe
delivers the packets on longer propagation paths. For example, with 0.09
GBPR is better in saving energy by 66% and 18% than EEF and EMGGR
Figure 9.b illustrates the average delay as a function of the packet injection rate.
traffic rates, GBPR is stable against the increase in the amount of traffic.
always favors nodes that advance the packets toward the sink node. In addition, the assumed
number of nodes (300 nodes) seems to be sufficient to
packets are propagated in the network in
average delay in EMGGR. However, in EEF increasing the traffic rate induces extra delay due to
the increased number of packets propagated in the network, which leads to collisions and high
propagation time. With 0.09 packets/s
(23%).
Figure 9.c shows the PDR as a function of traffic generation rate. Clearly
proportional to the packet injection rate, as expected. This happens because increasing the amount
of traffic increases the number of packets propagated in the network; consequently, the nodes
contend for the channel to deliver their packets. This increase in the congestion increases the
chance of packets’ collisions, which reduces the successfully received
Nevertheless, GBPR outperforms EEF and EMGGR in delivering data packets over all used
traffic rate. In addition, the decrease in the PDR in EFF is very sharp compared to the
protocol, and this proves the benefits of using a s
high traffic load) of each packet to avoid collisions between packets. For instance, when the
injection traffic rate is 0.09 packets/s
and 23%, respectively. Furthermore, the results shade some lights on the superiority of our
protocol under high traffic load conditions, and this makes it suitable for applications that require
frequent data transmissions from the deployed sensors such as
(a) (b)
0
10
20
30
40
50
60
70
80
90
0.05 0.07 0.09
Energyconsumption(KJ)
Traffic rate (packets/s)
Energy Consumption
GBPR EMGGR
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
As revealed in Figure 9.a, the energy consumption is positively proportional to the inj
all evaluated protocols. However, GBPR is the most energy efficient among the
three protocols. The key causes of the energy efficiency of GBPR are the lower number of control
, the selection of a single forwarding candidate in each hop, and the
feasible neighboring cell to the destination cell. The difference in the
GBPR and EEF is significant overall used traffic rates. However,
EMGGR incurs only a small increase in the energy consumption compared to GBPR
expected since both of them select a single candidate forwarder in each hop, howe
on longer propagation paths. For example, with 0.09 packets/s injection rate,
is better in saving energy by 66% and 18% than EEF and EMGGR, respectively.
Figure 9.b illustrates the average delay as a function of the packet injection rate. With the used
s, GBPR is stable against the increase in the amount of traffic. This is because
always favors nodes that advance the packets toward the sink node. In addition, the assumed
) seems to be sufficient to minimize the number of void cells; thus
packets are propagated in the network in fewer hops. This also explains the reduction in the
average delay in EMGGR. However, in EEF increasing the traffic rate induces extra delay due to
the increased number of packets propagated in the network, which leads to collisions and high
packets/s injection rate, GBPR outperforms EEF (EMGGR) by 52%
Figure 9.c shows the PDR as a function of traffic generation rate. Clearly, the PDR is inversely
proportional to the packet injection rate, as expected. This happens because increasing the amount
affic increases the number of packets propagated in the network; consequently, the nodes
contend for the channel to deliver their packets. This increase in the congestion increases the
chance of packets’ collisions, which reduces the successfully received packets at the sink node.
outperforms EEF and EMGGR in delivering data packets over all used
traffic rate. In addition, the decrease in the PDR in EFF is very sharp compared to the
protocol, and this proves the benefits of using a single forwarder (i.e. especially in networks with
high traffic load) of each packet to avoid collisions between packets. For instance, when the
packets/s, GBPR gives better PDR than EMGGR and EEF by 31%
Furthermore, the results shade some lights on the superiority of our
protocol under high traffic load conditions, and this makes it suitable for applications that require
frequent data transmissions from the deployed sensors such as monitoring application
(a) (b)
0.09 0.11
Traffic rate (packets/s)
Energy Consumption
EMGGR EEF
0
1
2
3
4
5
6
0.05 0.07 0.09
Averagedelay(s)
Traffic rate (packets/s)
Average end-to-end delay
GBPR EMGGR
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
13
As revealed in Figure 9.a, the energy consumption is positively proportional to the injection rate
is the most energy efficient among the
the lower number of control
, the selection of a single forwarding candidate in each hop, and the
The difference in the
all used traffic rates. However,
GBPR. This is
expected since both of them select a single candidate forwarder in each hop, however, EMGGR
injection rate,
, respectively.
With the used
This is because GBPR
always favors nodes that advance the packets toward the sink node. In addition, the assumed
the number of void cells; thus
ns the reduction in the
average delay in EMGGR. However, in EEF increasing the traffic rate induces extra delay due to
the increased number of packets propagated in the network, which leads to collisions and high
outperforms EEF (EMGGR) by 52%
the PDR is inversely
proportional to the packet injection rate, as expected. This happens because increasing the amount
affic increases the number of packets propagated in the network; consequently, the nodes
contend for the channel to deliver their packets. This increase in the congestion increases the
packets at the sink node.
outperforms EEF and EMGGR in delivering data packets over all used
traffic rate. In addition, the decrease in the PDR in EFF is very sharp compared to the GBPR
ingle forwarder (i.e. especially in networks with
high traffic load) of each packet to avoid collisions between packets. For instance, when the
gives better PDR than EMGGR and EEF by 31%
Furthermore, the results shade some lights on the superiority of our
protocol under high traffic load conditions, and this makes it suitable for applications that require
monitoring applications.
0.11
end delay
EEF
14. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
(a) Energy consumption
Impact of Nodes’ Mobility:
Figure 10.a depicts the impact of nodes’ mobility on the energy consumption. While EEF shows
a little increase in the energy consumption, both
with the increase in the nodes’ maximum speed. The reason is that with the increase in the nodes’
speed, the number of void cells in
propagated to their destinations. This decrease in the number of propagated packets reduces the
amount of consumed energy. In EEF, since the packets are broadcasted to all the nodes in the
range, new nodes can enter the range with the increase in the nodes’ speed, which slightly
increases the energy consumption. Generally,
other two protocols. Specifically, when the maximum speed of nodes is set to 1m/s,
to save energy up to 72% and 20% compared to the EEF and EMGGR, resp
Figure 10.b reveals the average end
there is an inverse proportionality between the speed of nodes and the average delay.
justified as follows. In GBPR and EMGGR, increasing the speed of nodes increases the number
of voids; hence, elections are performed frequently, and the number of dropped packets increases.
In other words, only a few packets get deliver
queuing delay are incurred. In EEF, the increase in the movement of the nodes reduces the
congestions in the channel since nodes change
incurred. However, as observed in the previous experiments (
provides the best performance compared to EEF and EMGGR protocols in terms of the average
delay. It is worth mentioning that when the maximum speed of nodes is set to 1 m/s,
outperforms EEF and EMGGR by 50%
Figure 10.c demonstrates the impact of nodes’ mobility on the PDR. As the maximum speed of
nodes increases, the three protocols show a decrease in the number of successfully received
packets. EMGGR and GBPR depen
of the nodes results in moving out of their cells, which results in initiating elections frequently.
This increases the number of control packets propagated in the network as well as increasing t
number of void cells. However, the decrease is sharper in EMGGR due to the longer propagation
paths; thus, the probability of the packets being dropped increases. In EEF, each forwarder
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
PDR(paket/s)
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
(c)
nergy consumption (b) Average end-to-end delay (c) PDR
Figure 9: Impact of traffic rate on
Figure 10.a depicts the impact of nodes’ mobility on the energy consumption. While EEF shows
a little increase in the energy consumption, both GBPR and EMGGR consume a little less energy
with the increase in the nodes’ maximum speed. The reason is that with the increase in the nodes’
speed, the number of void cells in GBPR and EMGGR increases; hence, fewer data packets are
tions. This decrease in the number of propagated packets reduces the
amount of consumed energy. In EEF, since the packets are broadcasted to all the nodes in the
range, new nodes can enter the range with the increase in the nodes’ speed, which slightly
reases the energy consumption. Generally, GBPR is the most energy efficient compared to the
other two protocols. Specifically, when the maximum speed of nodes is set to 1m/s, GBPR
to save energy up to 72% and 20% compared to the EEF and EMGGR, respectively.
Figure 10.b reveals the average end-to-end delay as a function of the nodes’ mobility. Generally,
there is an inverse proportionality between the speed of nodes and the average delay.
justified as follows. In GBPR and EMGGR, increasing the speed of nodes increases the number
of voids; hence, elections are performed frequently, and the number of dropped packets increases.
few packets get delivered to the destinations, and fewer congestions and
queuing delay are incurred. In EEF, the increase in the movement of the nodes reduces the
the channel since nodes change their positions; thus, the less queuing delay is
d in the previous experiments (Figure 8.b and Figure 9.b),
provides the best performance compared to EEF and EMGGR protocols in terms of the average
delay. It is worth mentioning that when the maximum speed of nodes is set to 1 m/s,
EF and EMGGR by 50% and 28%, respectively.
Figure 10.c demonstrates the impact of nodes’ mobility on the PDR. As the maximum speed of
nodes increases, the three protocols show a decrease in the number of successfully received
depend on cell-heads for forwarding the packets, and the movement
of the nodes results in moving out of their cells, which results in initiating elections frequently.
This increases the number of control packets propagated in the network as well as increasing t
number of void cells. However, the decrease is sharper in EMGGR due to the longer propagation
paths; thus, the probability of the packets being dropped increases. In EEF, each forwarder
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.05 0.07 0.09 0.11
Traffic rate (packets/s)
Packet Delivery Ratio (PDR)
GBPR EMGGR EEF
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
14
Figure 10.a depicts the impact of nodes’ mobility on the energy consumption. While EEF shows
and EMGGR consume a little less energy
with the increase in the nodes’ maximum speed. The reason is that with the increase in the nodes’
data packets are
tions. This decrease in the number of propagated packets reduces the
amount of consumed energy. In EEF, since the packets are broadcasted to all the nodes in the
range, new nodes can enter the range with the increase in the nodes’ speed, which slightly
compared to the
GBPR is able
end delay as a function of the nodes’ mobility. Generally,
there is an inverse proportionality between the speed of nodes and the average delay. This can be
justified as follows. In GBPR and EMGGR, increasing the speed of nodes increases the number
of voids; hence, elections are performed frequently, and the number of dropped packets increases.
congestions and
queuing delay are incurred. In EEF, the increase in the movement of the nodes reduces the
less queuing delay is
igure 9.b), GBPR
provides the best performance compared to EEF and EMGGR protocols in terms of the average
delay. It is worth mentioning that when the maximum speed of nodes is set to 1 m/s, GBPR
Figure 10.c demonstrates the impact of nodes’ mobility on the PDR. As the maximum speed of
nodes increases, the three protocols show a decrease in the number of successfully received
heads for forwarding the packets, and the movement
of the nodes results in moving out of their cells, which results in initiating elections frequently.
This increases the number of control packets propagated in the network as well as increasing the
number of void cells. However, the decrease is sharper in EMGGR due to the longer propagation
paths; thus, the probability of the packets being dropped increases. In EEF, each forwarder
15. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
broadcasts the packets to all its neighbors; hence, the probability
from the range is less. Generally,
speeds. For instance, when the maximum speed of nodes is set to 1 m/s,
deliver packets to the destinations than EEF a
(a) (b)
(a) Energy consumption (b)
Figure 10: Impact of nodes' mobility on
4.3.2 GBPR WITH MULTIPLE
The aim of this set of simulations is to study the effects of using multiple sink nodes on the
performance of the proposed protocol.
two and three sinks. 200, 300 and 4
Figure 11.a illustrates the energy efficiency of the protocol under the use of
sink nodes. Clearly, using multiple sinks provide better performance than using a single sink. The
0
10
20
30
40
50
60
70
80
90
0 0.5 1
Energyconsumption(KJ)
Speed of nodes (m/s)
Energy Consumption
GBPR EMGGR
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
PDR(packet/s)
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
broadcasts the packets to all its neighbors; hence, the probability of all neighbors being deviated
from the range is less. Generally, GBPR outperforms EEF and EMGGR in PDR over
speeds. For instance, when the maximum speed of nodes is set to 1 m/s, GBPR is superior to
deliver packets to the destinations than EEF and EMGGR by 15% and 49%, respectively.
) (b)
(c)
(a) Energy consumption (b) Average end-to-end delay (c) PDR
Figure 10: Impact of nodes' mobility on
ULTIPLE SINKS:
The aim of this set of simulations is to study the effects of using multiple sink nodes on the
performance of the proposed protocol. Specifically, the protocol is evaluated under the use of one,
00 and 400 static nodes are deployed randomly in the environment.
Figure 11.a illustrates the energy efficiency of the protocol under the use of a different number of
sink nodes. Clearly, using multiple sinks provide better performance than using a single sink. The
1.5
Speed of nodes (m/s)
Energy Consumption
EEF
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 0.5 1 1.5
Averagedelay(s)
Speed of nodes (m/s)
Average end-to-end delay
GBPR EMGGR EEF
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 0.5 1 1.5
Speed of nodes (m/s)
Packet Delivery Ratio (PDR)
GBPR EMGGR EEF
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
15
of all neighbors being deviated
forms EEF and EMGGR in PDR overall used
is superior to
nd EMGGR by 15% and 49%, respectively.
The aim of this set of simulations is to study the effects of using multiple sink nodes on the
Specifically, the protocol is evaluated under the use of one,
deployed randomly in the environment.
different number of
sink nodes. Clearly, using multiple sinks provide better performance than using a single sink. The
1.5
EEF
16. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
cause of this is that using multiple sinks allow nodes to send their packets to the closer sink;
hence, reduces the number of hops packet
the sinks is distributed, and the collisions between packets are reduced. On average, using two
sinks provides 5.3% better energy efficiency over scenarios with a single sink. However, the
improvement is just 0.3% when using three sinks
and the further increase in the number of sinks will not give much improvement in saving energy.
The reason behind this is that the number of sinks to be used depends on
area and its expansion in the horizontal plane. Basically, the larger the area, the more sinks need
to be used. Therefore, network developers need to link the number of sinks with the size of the
monitored area in order to achieve
The average end-to-end delay experienced by data packets received by the sink nodes is
demonstrated in Figure 11.b. As we can see from the figure, the scenario with three sinks
outperforms the other two scenarios with a single and tw
the packets are delivered successfully to the closer sink nod
propagation delay and back-off times. With 200 nodes, the improvement of using two sinks is
approximately 10.9% compared to the scenario with a single sink. However, the delay of three
sinks is 4.5% lower than that of two sinks. The gab in the differences reduces with the i
the number of nodes due to the resulting decrease in the number of void cells.
The PDR as a function of the number of nodes
Figure 11.c. The figure shows an increase in delivering data packets when multiple sinks are used
compared to the scenario with a single sink. Specifically, with two sin
in delivering 76% of the packets to the sink nodes even in scenarios of very sparse networks (i.e.
less than 200 nodes). Nevertheless, there is a slight increase when using three sinks compared
with the scenario of two sinks. In
is 6.6% overall used number of nodes compared with a single sink setup. However, the
improvement of scenarios of three sinks is just 1.3% over those with two sinks.
(a)
12
14
16
18
20
22
24
26
28
200 300
Energyconsumption(KJ)
Number of nodes
Energy Consumption
Two sinks Three sinks
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
cause of this is that using multiple sinks allow nodes to send their packets to the closer sink;
hence, reduces the number of hops packets need to make. In addition, the load on the nodes near
the sinks is distributed, and the collisions between packets are reduced. On average, using two
sinks provides 5.3% better energy efficiency over scenarios with a single sink. However, the
is just 0.3% when using three sinks over the use of two sinks. The trend is logical
further increase in the number of sinks will not give much improvement in saving energy.
The reason behind this is that the number of sinks to be used depends on the size of the monitored
area and its expansion in the horizontal plane. Basically, the larger the area, the more sinks need
to be used. Therefore, network developers need to link the number of sinks with the size of the
monitored area in order to achieve better performance.
end delay experienced by data packets received by the sink nodes is
demonstrated in Figure 11.b. As we can see from the figure, the scenario with three sinks
outperforms the other two scenarios with a single and two sinks. The justification of this is that
the packets are delivered successfully to the closer sink nodes via fewer hops, which decrease the
off times. With 200 nodes, the improvement of using two sinks is
mpared to the scenario with a single sink. However, the delay of three
sinks is 4.5% lower than that of two sinks. The gab in the differences reduces with the i
due to the resulting decrease in the number of void cells.
DR as a function of the number of nodes for a different number of sinks is examined in
Figure 11.c. The figure shows an increase in delivering data packets when multiple sinks are used
compared to the scenario with a single sink. Specifically, with two sinks, the protocol succeeded
in delivering 76% of the packets to the sink nodes even in scenarios of very sparse networks (i.e.
less than 200 nodes). Nevertheless, there is a slight increase when using three sinks compared
with the scenario of two sinks. In particular, the average improvement when using two sink nodes
all used number of nodes compared with a single sink setup. However, the
improvement of scenarios of three sinks is just 1.3% over those with two sinks.
(b)
400
Number of nodes
Energy Consumption
Three sinks Single sink
1
1.3
1.6
1.9
2.2
2.5
2.8
200 300 400
Delay(s)
Number of nodes
Average end-to-end delay
Single sink Two sinks Three sinks
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
16
cause of this is that using multiple sinks allow nodes to send their packets to the closer sink;
s need to make. In addition, the load on the nodes near
the sinks is distributed, and the collisions between packets are reduced. On average, using two
sinks provides 5.3% better energy efficiency over scenarios with a single sink. However, the
two sinks. The trend is logical,
further increase in the number of sinks will not give much improvement in saving energy.
the size of the monitored
area and its expansion in the horizontal plane. Basically, the larger the area, the more sinks need
to be used. Therefore, network developers need to link the number of sinks with the size of the
end delay experienced by data packets received by the sink nodes is
demonstrated in Figure 11.b. As we can see from the figure, the scenario with three sinks
o sinks. The justification of this is that
which decrease the
off times. With 200 nodes, the improvement of using two sinks is
mpared to the scenario with a single sink. However, the delay of three
sinks is 4.5% lower than that of two sinks. The gab in the differences reduces with the increase in
is examined in
Figure 11.c. The figure shows an increase in delivering data packets when multiple sinks are used
ks, the protocol succeeded
in delivering 76% of the packets to the sink nodes even in scenarios of very sparse networks (i.e.
less than 200 nodes). Nevertheless, there is a slight increase when using three sinks compared
ing two sink nodes
all used number of nodes compared with a single sink setup. However, the
400
end delay
Three sinks
17. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
(a) Energy consumption
Figure 11: Impact of varying the number of sinks on
5. CONCLUSION AND FUTURE
In this paper, we proposed a new
protocol in which the packet is forwarded on
protocol is the classification of the neighboring cells into priority levels
distances to the sink nodes. Basically, neighboring cells
higher priority to be selected as the next hop. Hence,
hops, and therefore high PDR, less energy and
GBPR implements a simple election algorithm with short control packets to
responsible for forwarding data packets to the sink nodes. In addition, to efficiently conserve the
available limited resources (e.g. energy and bandwidth), data packets are forwarded to a single
cell-head in each hop and cells with
evaluation results show that the proposed protocol achieves the best performanc
energy consumption, average end
experiments. Furthermore, multiple sinks scenarios provide better performance results over single
sink counterparts. Therefore, the
detection, disaster prevention and early warning, and deep
For future direction, we aim to investigate how to adaptively order the selection of the next
forwarder according to the channel conditions (e.g. amount of traffic) in order to further improve
the performance of the protocol. Furthermore, we plan to invest
enhanced protocol with other protocols available in the literature and under different network
conditions. In addition, we aim to find the optimal number of sink nodes to be used as well as
their optimal placement that will lea
might be more powerful and more expensive than sensor nodes; therefore, finding the optimal
number and optimal placement is a valuable study.
ACKNOWLEDGMENT
This work is supported by The R
research grant number RC/SCI/COMP/15/02.
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
PDR(packet/s)
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
(c)
a) Energy consumption (b) Average end-to-end delay (c) PDR
Figure 11: Impact of varying the number of sinks on
UTURE WORKS
proposed a new GBPR routing protocol for UWSNs. It is a grid-based routing
which the packet is forwarded on a cell-by-cell basis. The key feature of
classification of the neighboring cells into priority levels according to their
distances to the sink nodes. Basically, neighboring cells that are closer to the sink are given
higher priority to be selected as the next hop. Hence, packets are propagated in less number of
high PDR, less energy and low end-to-end delay are achieved.
implements a simple election algorithm with short control packets to select nodes that are
responsible for forwarding data packets to the sink nodes. In addition, to efficiently conserve the
ted resources (e.g. energy and bandwidth), data packets are forwarded to a single
head in each hop and cells with a positive advance toward the sink nodes are favored.
evaluation results show that the proposed protocol achieves the best performance in terms of
end-to-end delay and PDR than EEF and EMGGR over all
experiments. Furthermore, multiple sinks scenarios provide better performance results over single
the GBPR can suit a number of applications such as intruder
detection, disaster prevention and early warning, and deep-water oil drilling.
For future direction, we aim to investigate how to adaptively order the selection of the next
forwarder according to the channel conditions (e.g. amount of traffic) in order to further improve
the performance of the protocol. Furthermore, we plan to investigate the performance of the
enhanced protocol with other protocols available in the literature and under different network
conditions. In addition, we aim to find the optimal number of sink nodes to be used as well as
their optimal placement that will lead to better performance results. This is because sink nodes
might be more powerful and more expensive than sensor nodes; therefore, finding the optimal
number and optimal placement is a valuable study.
This work is supported by The Research Council (TRC) of the Sultanate of Oman under the
research grant number RC/SCI/COMP/15/02.
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
200 300 400
Number of nodes
Packet Delivery Ratio (PDR)
Single sink Two sinks Three sinks
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
17
based routing
The key feature of the GBPR
according to their
the sink are given
packets are propagated in less number of
end delay are achieved. Moreover,
elect nodes that are
responsible for forwarding data packets to the sink nodes. In addition, to efficiently conserve the
ted resources (e.g. energy and bandwidth), data packets are forwarded to a single
positive advance toward the sink nodes are favored. The
e in terms of
delay and PDR than EEF and EMGGR over all
experiments. Furthermore, multiple sinks scenarios provide better performance results over single
lications such as intruder
For future direction, we aim to investigate how to adaptively order the selection of the next
forwarder according to the channel conditions (e.g. amount of traffic) in order to further improve
igate the performance of the
enhanced protocol with other protocols available in the literature and under different network
conditions. In addition, we aim to find the optimal number of sink nodes to be used as well as
d to better performance results. This is because sink nodes
might be more powerful and more expensive than sensor nodes; therefore, finding the optimal
esearch Council (TRC) of the Sultanate of Oman under the
18. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
18
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AUTHORS
MS. Faiza Al-Salti received her B.Sc. and M.Sc. Degrees in computer science from the
Sultan Qaboos University (Oman) in 2012 and 2015, respectively. She is currently a
Ph.D. candidate in the Sultan Qaboos University. Her research interest include
communication protocols, terrestrial and underwater wireless sensor networks.
Dr. Nasser Alzeidi received his PhD degree in Computer Science from the University
of Glasgow (UK) in 2007. He is currently an assistant professor of computer science and
the director of the Center for Information Systems at Sultan Qaboos University, Oman.
His research interests include performance evaluation of communication systems,
wireless networks, interconnection networks, System on Chip architectures and parallel
and distributed computing. He is a member of the IEEE.
Prof. Khaled Day received his undergraduate degree in computer science from the
University of Tunis in 1986. He was awarded
Prize’ for excellent academic performance
degree. He was then awarded from the Tunisian government a scholarship to pursue
graduate studies in the USA starting 1987.
computer science from the University of Minnesota (U
respectively. Dr. Day has worked at the University of Bahrain in the period 1992
as Assistant Professor. He then joined in 1996 Sultan Qaboos University as Assistant Professor. He was
promoted to Associate Professor in 1999 and th
Department of Computer Science at Sultan Qaboos University during the periods 2000
2010. He was appointed in January 2013 as the Dean of Research of Sultan Qaboos University. His areas of
research interest include interconnection networks, parallel algorithms, distributed systems and wireless
networks. He has published over 100 research papers in international journals and conferences. He has
received in 2000 the Abdul Hameed Shoman Prize f
IEEE.
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
F. Al Salti, N. Alzeidi, and B. R. Arafeh, “EMGGR: an energy-efficient multipath grid
ocol for underwater wireless sensor networks,” Wireless Networks, February
J. Jiang, G. Han, H. Guo, L. Shu, and J. J. P. Rodrigues, “Geographic multipath routing based on
cycled underwater wireless sensor networks,” Journal of Networks and
Computer Applications, vol. 59, pp. 4–13, January 2016.
M. Tariq, M. ShafieAbdLatiff, M. Ayaz, Y. Coulibaly, and N. Al-Areqi, “Distance based Reliable and
Energy Efficient (DREE) Routing Protocol for Underwater Acoustic Sensor Networks,” Journal of
Networks, vol. 10, no. 5, pp. 311–321, May 2015.
P. Xie, Z. Zhou, Z. Peng, H. Yan, T. Hu, J.-H. Cui, Z. Shi, Y. Fei, and S. Zhou, “Aqua-
based simulator for underwater sensor networks,” Proceedings of MTS/IEEE Biloxi
Technology for Our Future: Global and Local Challenges (OCEANS 2009), 26-29 October 2009, pp.
“LinkQuest: Underwater acoustic modem models.” [Online]. Available: http://www.link
quest.com/html/models1.htm. [Accessed: 08-Feb-2016].
V. Davies, “Evaluating Mobility Models Within An Ad Hoc Network,” Master’s thesis, Colorado
T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,”
mmunications and Mobile Computing, vol. 2, no. 5, pp. 483–502, August 2002.
M. Xu, G. Liu, and H. Wu, “An Energy-Efficient Routing Algorithm for Underwater Wireless Sensor
Networks Inspired by Ultrasonic Frogs,” International Journal of Distributed Sensor Networks, vol.
2014, Article ID 351520, 12 pages, 2014.
H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless
networks for aquatic applications,” IEEE Network, vol. 20, no. 3, pp. 12–18, May 2006.
received her B.Sc. and M.Sc. Degrees in computer science from the
Sultan Qaboos University (Oman) in 2012 and 2015, respectively. She is currently a
Ph.D. candidate in the Sultan Qaboos University. Her research interest includes
communication protocols, terrestrial and underwater wireless sensor networks.
received his PhD degree in Computer Science from the University
of Glasgow (UK) in 2007. He is currently an assistant professor of computer science and
director of the Center for Information Systems at Sultan Qaboos University, Oman.
His research interests include performance evaluation of communication systems,
wireless networks, interconnection networks, System on Chip architectures and parallel
stributed computing. He is a member of the IEEE.
received his undergraduate degree in computer science from the
University of Tunis in 1986. He was awarded in 1986 the ‘President Habib Bourguiba
Prize’ for excellent academic performance upon completion of his undergraduate
degree. He was then awarded from the Tunisian government a scholarship to pursue
graduate studies in the USA starting 1987. He obtained the M.Sc. and Ph.D. degrees in
computer science from the University of Minnesota (USA) in 1989 and 1992
respectively. Dr. Day has worked at the University of Bahrain in the period 1992-1996
as Assistant Professor. He then joined in 1996 Sultan Qaboos University as Assistant Professor. He was
promoted to Associate Professor in 1999 and then to Professor in 2005. He served as the Head of the
Department of Computer Science at Sultan Qaboos University during the periods 2000-2002 and 2006
2010. He was appointed in January 2013 as the Dean of Research of Sultan Qaboos University. His areas of
research interest include interconnection networks, parallel algorithms, distributed systems and wireless
networks. He has published over 100 research papers in international journals and conferences. He has
Abdul Hameed Shoman Prize for Young Arab Researchers. He is a senior member of
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
19
efficient multipath grid-based
ocol for underwater wireless sensor networks,” Wireless Networks, February
J. Jiang, G. Han, H. Guo, L. Shu, and J. J. P. Rodrigues, “Geographic multipath routing based on
Journal of Networks and
Areqi, “Distance based Reliable and
Sensor Networks,” Journal of
-Sim: An NS-2
Biloxi - Marine
29 October 2009, pp.
“LinkQuest: Underwater acoustic modem models.” [Online]. Available: http://www.link-
V. Davies, “Evaluating Mobility Models Within An Ad Hoc Network,” Master’s thesis, Colorado
T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,”
502, August 2002.
Efficient Routing Algorithm for Underwater Wireless Sensor
ensor Networks, vol.
H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless
18, May 2006.
as Assistant Professor. He then joined in 1996 Sultan Qaboos University as Assistant Professor. He was
en to Professor in 2005. He served as the Head of the
2002 and 2006-
2010. He was appointed in January 2013 as the Dean of Research of Sultan Qaboos University. His areas of
research interest include interconnection networks, parallel algorithms, distributed systems and wireless
networks. He has published over 100 research papers in international journals and conferences. He has
He is a senior member of
20. International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
Dr. Bassel R. Arafeh received the B.Sc. in Electrical Engineering from Cairo
University, Egypt, in 1974; and the M.E. degree in Electrical Engineering and the
Ph.D. degree in Computer Science from
Texas in 1980 and 1986, respectively. Dr. Arafeh is currently an associate professor in
the Department of Computer Science, Sultan Qaboos University, Muscat, Oman. His
research interests include wireless ad hoc and
distributed computing systems. He is a member of the ACM, the IEEE and IEEE
Computer Society.
Dr. Abderezak Touzene received the BS degree in computer science from University
Algiers in 1987, MS degree in computer science from Paris
(France) in 1988 and Phd degree in computer science from Institutpolytechnique de
Grenobe (France) in 1992. He is an Associate. Prof. in Sultan Qaboos University,
Oman. Dr. Touzene’s research inc
Distributed Systems, Network-On-chip, Wireless and Mobile Networks, Performance
Evaluation, Cryptography. Dr. Touzene is a member of the IEEE Computer Society.
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
received the B.Sc. in Electrical Engineering from Cairo
University, Egypt, in 1974; and the M.E. degree in Electrical Engineering and the
Ph.D. degree in Computer Science from Texas A & M University, College Station,
Texas in 1980 and 1986, respectively. Dr. Arafeh is currently an associate professor in
the Department of Computer Science, Sultan Qaboos University, Muscat, Oman. His
research interests include wireless ad hoc and sensor networks, and parallel and
distributed computing systems. He is a member of the ACM, the IEEE and IEEE
received the BS degree in computer science from University
degree in computer science from Paris-SudOrsay University
(France) in 1988 and Phd degree in computer science from Institutpolytechnique de
He is an Associate. Prof. in Sultan Qaboos University,
Dr. Touzene’s research includes Interconnection Networks, Parallel and
chip, Wireless and Mobile Networks, Performance
Evaluation, Cryptography. Dr. Touzene is a member of the IEEE Computer Society.
International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
20