A fuzzy based congestion controller for control and balance congestion in gri...csandit
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited
power supply in a wide geographically area. These sensors collect information depending on
application. The sensors transmit the data towards a base station called sink. Due to the
relatively high node density and source-to-sink communication pattern, congestion is a critical
issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy
logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid
increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the
Monitor Node. Simulation results show that our approach has higher packet delivery ratio and
lower packet loss than existing approaches.
Priority based bandwidth allocation in wireless sensor networksIJCNCJournal
Most of the sensor network applications need real time communication and the need for deadline aware real time communication is becoming eminent in these applications. These applications have different dead line requirements also. The real time applications of wireless sensor networks are bandwidth sensitive and need higher share of bandwidth for higher priority data to meet the dead line requirements. In this paper we focus on the MAC layer modifications to meet the real time requirements of different priority data.Bandwidth partitioning among different priority transmissions is implemented through MAC layer modifications. The MAC layer implements a queuing model that supports lower transfer rate for lower
priority packets and higher transfer rate for real
time packets with higher priority, minimizing the end to
end delay. The performance of the algorithm is evaluated with varying node distribution
.
PROPOSED A HETEROGENEOUS CLUSTERING ALGORITHM TO IMPROVE QOS IN WSNIJCNCJournal
In this article it has presented leach extended hierarchical 3-level clustered heterogeneous and dynamics
algorithm. On suggested protocol (LEH3LA) with planning of selected auction cluster head, and
alternative cluster head node, problem of delay on processing, processing of selecting members, decrease
of expenses, and energy consumption, decrease of sending message, and receiving messages inside the
clusters, selecting of cluster heads in large sensor networks were solved. This algorithm uses hierarchical
heterogeneous network (3-levels), collective intelligence, and intra-cluster interaction for communications.
Also it will solve the problems of sending data in Multi-BS mobile networks, expanding inter-cluster
networks, overlap cluster, genesis orphan nodes, boundary change dynamically clusters, using backbone
networks, cloud sensor. Using sleep/wake scheduling algorithm or TDMA-schedule alternative cluster head
node provides redundancy, and fault tolerance. Local processing in cluster head nodes, and alternative
cluster head, intra-cluster and inter-cluster communications such as Multi-HOP cause increase on
processing speed, and sending data intra-cluster and inter-cluster. Decrease of overhead network, and
increase the load balancing among cluster heads. Using encapsulation of data method, by cluster head
nodes, energy consumption decrease during sending data. Also by improving quality of service (QoS) in
CBRP, LEACH, 802.15.4, decrease of energy consumption in sensors, cluster heads and alternative cluster
head nodes, cause increase on lift time of sensor networks.
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.
REAL-TIME ROUTING PROTOCOLS FOR WIRELESS SENSOR NETWORKS: A SURVEYcscpconf
Wireless sensor networks can be termed as a new generation of distributed embedded systems
that has a capability of meeting broad range of real-time applications. Examples include
radiation monitoring, fire monitoring, border surveillance, and medical care to name but a few.
Wireless sensor networks that are deployed in time/mission-critical applications with highly
dynamic environments have to interact with the physical phenomenon under stringent timing
constraints and severe resource limitations. For such real-time wireless sensor networks,
designing and developing a real-time routing protocol that meets the required real-time
guarantee of data packets communication is a stimulating field of study that raised many
challenges and research issues. In this paper, we present a comprehensive survey of real-time
routing protocols in WSN, by discussing each protocol with its key features. Finally, we concluded this paper with open research issues and challenges of real-time routing in WSN.
Hexagonal based Clustering for Reducing Rebroadcasts in Mobile Ad Hoc NetworksIJTET Journal
Abstract— In mobile ad hoc networks multihop routing is performed in order to communicate the packets from the source to destination. The nodes within these networks are dynamic due which frequent path change occurs which can cause frequent link breakages and induces route discoveries. These route discoveries can introduce overhead in terms of contention, collision and rebroadcasts which are non-negligible. Here, the paper discusses a hexagonal based clustering for reducing rebroadcasts thus maximizing the lifetime of the networks and providing coverage area thus reducing the end – end delays.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
A fuzzy based congestion controller for control and balance congestion in gri...csandit
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited
power supply in a wide geographically area. These sensors collect information depending on
application. The sensors transmit the data towards a base station called sink. Due to the
relatively high node density and source-to-sink communication pattern, congestion is a critical
issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy
logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid
increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the
Monitor Node. Simulation results show that our approach has higher packet delivery ratio and
lower packet loss than existing approaches.
Priority based bandwidth allocation in wireless sensor networksIJCNCJournal
Most of the sensor network applications need real time communication and the need for deadline aware real time communication is becoming eminent in these applications. These applications have different dead line requirements also. The real time applications of wireless sensor networks are bandwidth sensitive and need higher share of bandwidth for higher priority data to meet the dead line requirements. In this paper we focus on the MAC layer modifications to meet the real time requirements of different priority data.Bandwidth partitioning among different priority transmissions is implemented through MAC layer modifications. The MAC layer implements a queuing model that supports lower transfer rate for lower
priority packets and higher transfer rate for real
time packets with higher priority, minimizing the end to
end delay. The performance of the algorithm is evaluated with varying node distribution
.
PROPOSED A HETEROGENEOUS CLUSTERING ALGORITHM TO IMPROVE QOS IN WSNIJCNCJournal
In this article it has presented leach extended hierarchical 3-level clustered heterogeneous and dynamics
algorithm. On suggested protocol (LEH3LA) with planning of selected auction cluster head, and
alternative cluster head node, problem of delay on processing, processing of selecting members, decrease
of expenses, and energy consumption, decrease of sending message, and receiving messages inside the
clusters, selecting of cluster heads in large sensor networks were solved. This algorithm uses hierarchical
heterogeneous network (3-levels), collective intelligence, and intra-cluster interaction for communications.
Also it will solve the problems of sending data in Multi-BS mobile networks, expanding inter-cluster
networks, overlap cluster, genesis orphan nodes, boundary change dynamically clusters, using backbone
networks, cloud sensor. Using sleep/wake scheduling algorithm or TDMA-schedule alternative cluster head
node provides redundancy, and fault tolerance. Local processing in cluster head nodes, and alternative
cluster head, intra-cluster and inter-cluster communications such as Multi-HOP cause increase on
processing speed, and sending data intra-cluster and inter-cluster. Decrease of overhead network, and
increase the load balancing among cluster heads. Using encapsulation of data method, by cluster head
nodes, energy consumption decrease during sending data. Also by improving quality of service (QoS) in
CBRP, LEACH, 802.15.4, decrease of energy consumption in sensors, cluster heads and alternative cluster
head nodes, cause increase on lift time of sensor networks.
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.
REAL-TIME ROUTING PROTOCOLS FOR WIRELESS SENSOR NETWORKS: A SURVEYcscpconf
Wireless sensor networks can be termed as a new generation of distributed embedded systems
that has a capability of meeting broad range of real-time applications. Examples include
radiation monitoring, fire monitoring, border surveillance, and medical care to name but a few.
Wireless sensor networks that are deployed in time/mission-critical applications with highly
dynamic environments have to interact with the physical phenomenon under stringent timing
constraints and severe resource limitations. For such real-time wireless sensor networks,
designing and developing a real-time routing protocol that meets the required real-time
guarantee of data packets communication is a stimulating field of study that raised many
challenges and research issues. In this paper, we present a comprehensive survey of real-time
routing protocols in WSN, by discussing each protocol with its key features. Finally, we concluded this paper with open research issues and challenges of real-time routing in WSN.
Hexagonal based Clustering for Reducing Rebroadcasts in Mobile Ad Hoc NetworksIJTET Journal
Abstract— In mobile ad hoc networks multihop routing is performed in order to communicate the packets from the source to destination. The nodes within these networks are dynamic due which frequent path change occurs which can cause frequent link breakages and induces route discoveries. These route discoveries can introduce overhead in terms of contention, collision and rebroadcasts which are non-negligible. Here, the paper discusses a hexagonal based clustering for reducing rebroadcasts thus maximizing the lifetime of the networks and providing coverage area thus reducing the end – end delays.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Transport layer protocol for urgent data transmission in wsneSAT Journals
Abstract wireless sensor networks is a growing class of highly dynamic, complex network environment on top of which a wide range of applications, such as habitat monitoring, object tracking, precision agriculture, building monitoring and military systems are built. The real time applications often generate urgent data and one-time event notifications that need to be communicated reliably. The successful delivery of such information has a direct effect on the overall performance of the system. Reliable communication is important for sensor networks. Urgent data transmission has been a serious problem for Wireless sensor networks. WSN face difficulties in handling urgent data like congestion and reliability due to their unique requirements and constraints. Various protocols for congestion avoidance and reliability achievement for WSN have been proposed recently. Few of them have also worked on congestion elimination. These protocols try to minimize the problem using different mechanism. This paper explores these mechanisms and tries to find their features and limitations which directed us for our research. Keywords: Congestion, Reliability, Transport layer Protocol, Urgent data transmission, Wireless Sensor Network.
Mtadf multi hop traffic aware data for warding for congestion control in wir...ijwmn
In the past few years there is a remarkable change in the field of wireless sensor networks. Congestion occurs when there is a heavy traffic in the network. The heavy traffic in the network leads to wastage of energy and packet loss. Traffic Aware Dynamic Routing algorithm mitigates congestion by using one hop neighbor routing, hence throughput of the network is low. This paper proposed a Multi hop based Data Forwarding Technique to mitigate congestion. Queue length field and depth potential field play a major role to divert the traffic in the network to the alternate paths. The high traffic load leads to data queue overflow in the sensor nodes, these results in loss of important information about important events. Multi hop Traffic-Aware Dynamic Routing algorithm addresses congestion using depth potential field and queue length potential field. The algorithm forwards data packets around the congestion areas and scatters the excessive along multiple paths. The nodes with less load are efficiently utilized in response to congestion. The main aspect of the algorithm is to construct two independent potential fields using 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. Simulations are conducted to evaluate the performance of our proposed algorithm and our proposed scheme performs better compared to previous work.
The Wireless Sensor Network is one of the most significant purposes behind the accomplishment of long range wireless communication. Frequent connectivity failures are occurred in the sensor-organised network due to obstruction, snags, message drop because of node energy depletion; obstacle and so forth. The total communication gets collapsed if there any lessening in the nature of correspondence or quality between the sensor nodes or from the sensor nodes to the sink nodes and this prompts to connection failures. To overcome the frequent connectivity failures we propose Diminishing Connectivity Failures by Auto-Reconfiguration in WSN (DCFA). This scheme provides steadfast routes to reduce the connectivity failure and improve the network performance.
Analysis of Cluster Based Anycast Routing Protocol for Wireless Sensor NetworkIJMER
A wireless sensor network is a collection of nodes organized into a cooperative network.
Each node consists of processing capability, may contain multiple types of memory, have a RF
transceiver, have a power source, and accommodate various sensors and actuators. The nodes
communicate wirelessly and often self-organize after being deployed in an ad hoc fashion.
Routing protocols for wireless sensor networks are responsible for maintaining the routes in the
network and have to ensure reliable multi-hop communication .The performance of the network is
greatly influenced by the routing techniques. Routing is to find out the path to route the sensed data to
the base station. In this paper the features of WSNs are introduced and routing protocols are reviewed
for Wireless Sensor Network.
A cross layer optimized reliable multicast routing protocol in wireless mesh ...ijdpsjournal
The Optimal way to create a protocol in Wireless Mesh Networks
is to take into account a cross layer due
to the interference among wireless transmissions.
In this paper
,
w
e focus on designing and implementing
a
reliable
multicast protocol
called Me
sh Reliable Multicast Protocol (MRMP).
A
recovery tree
built
dynamically
which is joining with
the multicast routing tree.
U
sing the recovery tree
the packet losses are
repaired locally
.
This Cross layer
Technique
between network layer (
multicast routing)
and transport layer
(
reliability) using simulation
results
prove
the effectiveness
and optimization
of
cross layer
in WMNs
compare with the conventional layer
EFFICIENT REBROADCASTING USING TRUSTWORTHINESS OF NODE WITH NEIGHBOUR KNOWLED...ijiert bestjournal
Mobile Ad hoc network is an infrastructure less communication network with limited resources. To maintain virtual
infrastructure for communication broadcasting mechanisms is used. Due to lack of energy efficiency in Mobile Ad
hoc network, there is a need to develop an efficient broadcasting model which enhances energy efficiency. Also
nodes with malicious behaviour cause an internal threat that disobeys the standard and degrades the performance of
routing protocols. This paper introduced an enhanced rebroadcasting algorithm, where rebroadcasting decision for
next hop is immediate or delayed on the basis of trust value and energy level of particular node. This approach helps
to decrease number of rebroadcast, energy consumption and also enhances security. The decision is made with trust
value associated with node, their remaining energy and total number of uncovered nodes.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
PERFORMANCE ANALYSIS OF CHANNEL ACCESS MODEL FOR MAC IN RANDOMLY DISTRIBUTED ...IJCNCJournal
Medium Access control (MAC) is one of the fundamental problems in wireless sensor networks. The performance of wireless sensor network depends on it. The main objective of a medium access control method is to provide high throughput, minimize the delay, and conservers the energy consumption by avoiding the collisions. In this paper, a general model for MAC protocol to reduce the delay, maximize throughput and conserve the energy consumption in channel accessing in high density randomly distributed wireless sensor network is presented. The proposed model is simulated using MATLAB. The simulation results show that the average delay for sensors with sufficient memory is lower than sensors without
memory. Further, the throughput of the channel access method with memory is better than without memory.
Improvement In LEACH Protocol By Electing Master Cluster Heads To Enhance The...Editor IJCATR
In wireless sensor networks, sensor nodes play the most prominent role. These sensor nodes are mainly un-chargeable, so it
raises an issue regarding lifetime of the network. Mainly sensor nodes collect data and transmit it to the Base Station. So, most of the
energy is consumed in the communication process between sensor nodes and the Base Station. In this paper, we present an
improvement on LEACH protocol to enhance the network lifetime. Our goal is to reduce the transmissions between cluster heads and
the sink node. We will choose optimum number of Master Cluster Heads from variation cluster heads present in the network. The
simulation results show that our proposed algorithm enhances the network lifetime as compare to the LEACH protocol.
ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...ijcsa
The most emerging prominent sensor network applications collect data from sensor nodes and monitors
periodically. Resource constraint Sensor motes sense the environment and transit data to the remote sink
via multiple hops. Minimum energy dissipation and secure data transmission are crucial to such
applications. This paper delivers an energy efficient, lifetime improving, secure periodic Data Gathering
scheme that is a hybrid of heuristic path establishment and secure data transmission. This protocol uses
artificial intelligence (AI) based A* heuristic search algorithm to establish energy efficient admissible
optimal path to sink in terms of high residual energy, minimum hop counts and high link quality. This
scheme also adopts block encryption Rivest Cipher (RC6) Algorithm to secure the transmission of packets.
This code and speed optimized block encryption provides confidentiality against critical data and
consumes less energy for encryption. This proposed method increases the network lifetime there by
reducing the total traffic load. Evaluation of performance analysis of this algorithm using Network
Simulator (NS2) shows the superiority of the proposed scheme
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Transport layer protocol for urgent data transmission in wsneSAT Journals
Abstract wireless sensor networks is a growing class of highly dynamic, complex network environment on top of which a wide range of applications, such as habitat monitoring, object tracking, precision agriculture, building monitoring and military systems are built. The real time applications often generate urgent data and one-time event notifications that need to be communicated reliably. The successful delivery of such information has a direct effect on the overall performance of the system. Reliable communication is important for sensor networks. Urgent data transmission has been a serious problem for Wireless sensor networks. WSN face difficulties in handling urgent data like congestion and reliability due to their unique requirements and constraints. Various protocols for congestion avoidance and reliability achievement for WSN have been proposed recently. Few of them have also worked on congestion elimination. These protocols try to minimize the problem using different mechanism. This paper explores these mechanisms and tries to find their features and limitations which directed us for our research. Keywords: Congestion, Reliability, Transport layer Protocol, Urgent data transmission, Wireless Sensor Network.
Mtadf multi hop traffic aware data for warding for congestion control in wir...ijwmn
In the past few years there is a remarkable change in the field of wireless sensor networks. Congestion occurs when there is a heavy traffic in the network. The heavy traffic in the network leads to wastage of energy and packet loss. Traffic Aware Dynamic Routing algorithm mitigates congestion by using one hop neighbor routing, hence throughput of the network is low. This paper proposed a Multi hop based Data Forwarding Technique to mitigate congestion. Queue length field and depth potential field play a major role to divert the traffic in the network to the alternate paths. The high traffic load leads to data queue overflow in the sensor nodes, these results in loss of important information about important events. Multi hop Traffic-Aware Dynamic Routing algorithm addresses congestion using depth potential field and queue length potential field. The algorithm forwards data packets around the congestion areas and scatters the excessive along multiple paths. The nodes with less load are efficiently utilized in response to congestion. The main aspect of the algorithm is to construct two independent potential fields using 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. Simulations are conducted to evaluate the performance of our proposed algorithm and our proposed scheme performs better compared to previous work.
The Wireless Sensor Network is one of the most significant purposes behind the accomplishment of long range wireless communication. Frequent connectivity failures are occurred in the sensor-organised network due to obstruction, snags, message drop because of node energy depletion; obstacle and so forth. The total communication gets collapsed if there any lessening in the nature of correspondence or quality between the sensor nodes or from the sensor nodes to the sink nodes and this prompts to connection failures. To overcome the frequent connectivity failures we propose Diminishing Connectivity Failures by Auto-Reconfiguration in WSN (DCFA). This scheme provides steadfast routes to reduce the connectivity failure and improve the network performance.
Analysis of Cluster Based Anycast Routing Protocol for Wireless Sensor NetworkIJMER
A wireless sensor network is a collection of nodes organized into a cooperative network.
Each node consists of processing capability, may contain multiple types of memory, have a RF
transceiver, have a power source, and accommodate various sensors and actuators. The nodes
communicate wirelessly and often self-organize after being deployed in an ad hoc fashion.
Routing protocols for wireless sensor networks are responsible for maintaining the routes in the
network and have to ensure reliable multi-hop communication .The performance of the network is
greatly influenced by the routing techniques. Routing is to find out the path to route the sensed data to
the base station. In this paper the features of WSNs are introduced and routing protocols are reviewed
for Wireless Sensor Network.
A cross layer optimized reliable multicast routing protocol in wireless mesh ...ijdpsjournal
The Optimal way to create a protocol in Wireless Mesh Networks
is to take into account a cross layer due
to the interference among wireless transmissions.
In this paper
,
w
e focus on designing and implementing
a
reliable
multicast protocol
called Me
sh Reliable Multicast Protocol (MRMP).
A
recovery tree
built
dynamically
which is joining with
the multicast routing tree.
U
sing the recovery tree
the packet losses are
repaired locally
.
This Cross layer
Technique
between network layer (
multicast routing)
and transport layer
(
reliability) using simulation
results
prove
the effectiveness
and optimization
of
cross layer
in WMNs
compare with the conventional layer
EFFICIENT REBROADCASTING USING TRUSTWORTHINESS OF NODE WITH NEIGHBOUR KNOWLED...ijiert bestjournal
Mobile Ad hoc network is an infrastructure less communication network with limited resources. To maintain virtual
infrastructure for communication broadcasting mechanisms is used. Due to lack of energy efficiency in Mobile Ad
hoc network, there is a need to develop an efficient broadcasting model which enhances energy efficiency. Also
nodes with malicious behaviour cause an internal threat that disobeys the standard and degrades the performance of
routing protocols. This paper introduced an enhanced rebroadcasting algorithm, where rebroadcasting decision for
next hop is immediate or delayed on the basis of trust value and energy level of particular node. This approach helps
to decrease number of rebroadcast, energy consumption and also enhances security. The decision is made with trust
value associated with node, their remaining energy and total number of uncovered nodes.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
PERFORMANCE ANALYSIS OF CHANNEL ACCESS MODEL FOR MAC IN RANDOMLY DISTRIBUTED ...IJCNCJournal
Medium Access control (MAC) is one of the fundamental problems in wireless sensor networks. The performance of wireless sensor network depends on it. The main objective of a medium access control method is to provide high throughput, minimize the delay, and conservers the energy consumption by avoiding the collisions. In this paper, a general model for MAC protocol to reduce the delay, maximize throughput and conserve the energy consumption in channel accessing in high density randomly distributed wireless sensor network is presented. The proposed model is simulated using MATLAB. The simulation results show that the average delay for sensors with sufficient memory is lower than sensors without
memory. Further, the throughput of the channel access method with memory is better than without memory.
Improvement In LEACH Protocol By Electing Master Cluster Heads To Enhance The...Editor IJCATR
In wireless sensor networks, sensor nodes play the most prominent role. These sensor nodes are mainly un-chargeable, so it
raises an issue regarding lifetime of the network. Mainly sensor nodes collect data and transmit it to the Base Station. So, most of the
energy is consumed in the communication process between sensor nodes and the Base Station. In this paper, we present an
improvement on LEACH protocol to enhance the network lifetime. Our goal is to reduce the transmissions between cluster heads and
the sink node. We will choose optimum number of Master Cluster Heads from variation cluster heads present in the network. The
simulation results show that our proposed algorithm enhances the network lifetime as compare to the LEACH protocol.
ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...ijcsa
The most emerging prominent sensor network applications collect data from sensor nodes and monitors
periodically. Resource constraint Sensor motes sense the environment and transit data to the remote sink
via multiple hops. Minimum energy dissipation and secure data transmission are crucial to such
applications. This paper delivers an energy efficient, lifetime improving, secure periodic Data Gathering
scheme that is a hybrid of heuristic path establishment and secure data transmission. This protocol uses
artificial intelligence (AI) based A* heuristic search algorithm to establish energy efficient admissible
optimal path to sink in terms of high residual energy, minimum hop counts and high link quality. This
scheme also adopts block encryption Rivest Cipher (RC6) Algorithm to secure the transmission of packets.
This code and speed optimized block encryption provides confidentiality against critical data and
consumes less energy for encryption. This proposed method increases the network lifetime there by
reducing the total traffic load. Evaluation of performance analysis of this algorithm using Network
Simulator (NS2) shows the superiority of the proposed scheme
A FUZZY-BASED CONGESTION CONTROLLER FOR CONTROL AND BALANCE CONGESTION IN GRI...cscpconf
A Wireless Sensor Network (WSN) is deployed with a large number of sensors with limited power supply in a wide geographically area. These sensors collect information depending on application. The sensors transmit the data towards a base station called sink. Due to the relatively high node density and source-to-sink communication pattern, congestion is a critical issue in WSN. Congestion not only causes packet loss, but also leads to excessive energy
consumption as well as delay. To address this problem, in this paper we propose a new fuzzy logic based mechanism to detect and control congestion in WSN. In the proposed approach, a
Monitor Node for each grid in congestion candidate region performs a fuzzy control to avoid increasing congestion. Fuzzy controller’s inputs are continually fetched from the network by the Monitor Node. Simulation results show that our approach has higher packet delivery ratio and lower packet loss than existing approaches.
TTACCA: TWO-HOP BASED TRAFFIC AWARE CONGESTION CONTROL ALGORITHM FOR WIRELESS...cscpconf
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.
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.
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
An efficient reconfigurable geographic routing congestion control algorithm f...IJECEIAES
In recent times, huge data is transferred from source to destination through multi path in wireless sensor networks (WSNs). Due to this more congestion occurs in the communication path. Hence, original data will be lost and delay problems arise at receiver end. The above-mentioned drawbacks can be overcome by the proposed efficient reconfigurable geographic routing congestion control (RgRCC) algorithm for wireless sensor networks. the proposed algorithm efficiently finds the node’s congestion status with the help queue length’s threshold level along with its change rate. Apart from this, the proposed algorithm re-routes the communication path to avoid congestion and enhances the strength of scalability of data communication in WSNs. The proposed algorithm frequently updates the distance between the nodes and by-pass routing holes, common for geographical routing. when the nodes are at the edge of the hole, it will create congestion between the nodes in WSNs. Apart from this, more nodes sink due to congestion. it can be reduced with the help of the proposed RgRCC algorithm. As per the simulation analysis, the proposed work indicates improved performance in comparison to conventional algorithm. By effectively identifying the data congestion in WSNs with high scalability rate as compared to conventional methods
Evaluating feasibility of using wireless sensor networks in a coffee crop thr...IJCNCJournal
A Wireless Sensor Networks is a network formed with sensors that have characteristics to sensor an area to
extract a specific metric, depending of the application.
We would like to analyse the feasibility to use sensors in a coffee crop.In this work we are evaluating routing protocolsusing real dimensions and characteristics of a coffee crop. We evaluate, through simulation, AODV, DSDV and AOMDV and two variants known in this work as AODVMOD and AOMDVMOD with 802.15.4 MAC Protocol
.For this comparison, we defined three performance metrics: Packet Delivery Ratio (PDR), End-to-End Delay
and Average Energy Consumption. Simulation results show that AOMDVMOD overall, outperforms others
routing protocols evaluated, showing that is possible to use WSN in a real coffee crop environment.
Quadrant Based DIR in CWin Adaptation Mechanism for Multihop Wireless NetworkIJCI JOURNAL
In Multihop Wireless Networks, traffic forwarding capability of each node varies according to its level of contention. Each node can yield its channel access opportunity to its neighbouring nodes, so that all the nodes can evenly share the channel and have similar forwarding capability. In this manner the wireless channel is utilized effectively, which is achieved using Contention Window Adaptation Mechanism (CWAM). This mechanism achieves a higher end-to-end throughout but consumes the network power to a higher level. So, a newly proposed algorithm Quadrant- Based Directional Routing Protocol (Q-DIR) is implemented as a cross-layer with CWAM, to reduce the total network power consumption through limited flooding and also reduce the routing overheads, which eventually increases overall network throughput. This algorithm limits the broadcast region to a quadrant where the source node and the destination nodes are located. Implementation of the algorithm is done in Linux based NS-2 simulator
A Professional QoS Provisioning in the Intra Cluster Packet Level Resource Al...GiselleginaGloria
Wireless mesh networking has transpired as a gifted technology for potential broadband wireless access. In a communication network, wireless mesh network plays a vital role in transmission and are structured in a mesh topology. The coordination of mesh routers and mesh clients forms the wireless mesh networks which are routed through the gateways. Wireless mesh networks uses IEEE 802.11 standards and has its wide applications broadband home networking and enterprise networking deployment such as Microsoft wireless mesh and MIT etc. A professional Qos provisioning in intra cluster packet level resource allocation for WMN approach takes power allocation, sub carrier allocation and packet scheduling. This approach combines the merits of a Karush-Kuhn-Tucker (KKT) algorithm and a genetic algorithm (GA) based approach. The KKT algorithm uses uniform power allocation over all the subcarriers, based on the optimal allocation criterion. The genetic algorithm is used to generate useful solutions to optimization and search problems and it is also used for search problems. By combining the intrinsic worth of both the approaches, it facilitates effective QOS provisioning at the packet level. It is concluded that, this approach achieves a preferred stability between system implementation and computational convolution.
Scheduling different types of packets, such as
real-time and non-real-time data packets, at sensor nodes with
resource constraints in Wireless Sensor Networks (WSN) is of
vital importance to reduce sensors’ energy consumptions and endto-end
data transmission delays. Most of the existing packetscheduling
mechanisms of WSN use First Come First Served
(FCFS), non pre-emptive priority and pre-emptive priority
scheduling algorithms. These algorithms incur a high processing
overhead and long end-to-end data transmission delay due to the
FCFS concept, starvation of high priority real-time data packets
due to the transmission of a large data packet in non pre-emptive
priority scheduling, starvation of non-real-time data packets due
to the probable continuous arrival of real-time data in preemptive
priority scheduling, and improper allocation of data
packets to queues in multilevel queue scheduling algorithms.
Moreover, these algorithms are not dynamic to the changing
requirements of WSN applications since their scheduling policies
are predetermined.
In the Advanced Multilevel Priority packet scheduling
scheme, each node except those at the last level has three levels of
priority queues. According to the priority of the packet and
availability of the queue, node will schedule the packet for
transmission. Due to separated queue availability, packet
transmission delay is reduced. Due to reduction in packet
transmission delay, node can goes into sleep mode as soon as
possible. And Expired packets are deleted at the particular node
at itself before reaching the base station, so that processing
burden on the node is reduced. Thus, energy of the node is saved.
Performance Comparison and Analysis of Mobile Ad Hoc Routing ProtocolsCSEIJJournal
A mobile ad hoc network (MANET) is a wireless network that uses multi-hop peer-to-peer routing instead
of static network infrastructure to provide network connectivity. MANETs have applications in rapidly
deployed and dynamic military and civilian systems. The network topology in a MANET usually changes
with time. Therefore, there are new challenges for routing protocols in MANETs since traditional routing
protocols may not be suitable for MANETs. Researchers are designing new MANET routing protocols
and comparing and improving existing MANET routing protocols before any routing protocols are
standardized using simulations. However, the simulation results from different research groups are not
consistent with each other. This is because of a lack of consistency in MANET routing protocol models
and application environments, including networking and user traffic profiles. Therefore, the simulation
scenarios are not equitable for all protocols and conclusions cannot be generalized. Furthermore, it is
difficult for one to choose a proper routing protocol for a given MANET application. According to the
aforementioned issues, this paper focuses on MANET routing protocols. Specifically, my contribution
includes the characterization of different routing protocols and compare and analyze the performance of
different routing protocols.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
1. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 7, July 2013
2311
www.ijarcet.org
Improving End-to-End Delay Distribution in Wireless
Sensor Networks.
R.J Lawande1
, A.H. Ansari2
1
PDVVPCOE, Ahmednagar, Maharashtra,India
2
P.R.E.C, Loni, Ahmednagar, Maharashtra,India.
Abstract— In today’s world emerging applications of
wireless sensor networks (WSNs) require real-time Qos.
average delay and end to end delay distribution is
important in WSNS. In a typical delay monitoring WSNS,
multiple reports are generated by several nodes when a
physical event occurs, and are then forwarded through
multi-hop communication to a sink that detects the event.
To improve the delay detection reliability, usually timely
delivery of a certain number of packets is required. The
previous delay analysis papers fail to give the single hop
delay distribution, also the busty traffic is not considered
so in this paper, a comprehensive cross-layer analysis
framework is used. The simulations on Network Simulator
2 show the average and end to end delay for both
deterministic and random deployments. Our model gives
closed form expressions for obtaining the average delay
and End to End delay characteristics and models each
node as a discrete time queue. Moreover, the simulation
and experiments show the Throughput and the packet loss
in WSNs. In this paper, the comparison of the CSMA/CA
Mac protocol and cross layer protocol for average delay
and End to end dealy,Throuhput and Packet loss is done
for WSNs.
Index Terms— Average Delay distribution, End to end
delay, real time systems, Throughput, wireless sensor
networks.
I. INTRODUCTION
Real time quality of service is necessary and
important for wireless sensor networks. The wireless sensor
networks are extensively used in the connectivity
infrastructure and distributed data network.Timing and
reliability are the two important factors for the quality of
service gurantees.To characterize average delay and end to
end delay distribution is fundamental for the real time
Ravindra J . lawande1
, Department of Electronics and Telecommunication
PDVVPCOE, Ahmednagar, Maharashtra, India
Abdul H. Ansari2
, Department of Electronics and Telecommunication, PREC
Loni, Maharashtra, India
communication applications with the probabilistic QoS
guarantees. Also to calculate the Throughput and the packet
loss is important for the real time wireless sensor networks
applications.[3] First, a accurate and reliable cross layer
framework is developed to characterize the average delay and
end to end delay distribution in both deterministic and random
deployments of nodes.[1] Second, Throughput and the Packet
loss of the CSMA/CA Mac protocol and Cross layer protocol
is calculated by the graphical analysis.
In existing system, CSMA/CA Mac protocol is
conducted to illustrate how developed framework can
analytically predict the distribution of the end-to-end
delay.[1][2] It does not give the guarantee of quality of
service. In proposed system, present comprehensive cross-
layer analysis framework, which employs a stochastic queuing
model in realistic channel environments, is developed for
average delay and end to end delay in WSNs .The cumulative
distribution function (cdf) of the delay can be used as a metric
to calculate delay. The end-to-end delay distribution depends
on the deterministic deployment and random deployment. For
both deployments, focus on the steady-state behavior of the
routing protocol.
This paper is organized as follows. Section I gives
the introduction. Section II reviews some previous work of
end-to-end delay. Section III introduces the software system
used for delay analysis. Section IV gives results of proposed
system. Section V concludes this paper.
II. Literature Survey
Yunbo Wang Mehmet C. Vuran Steve Goddard [1] have
proposed To improve the event detection reliability, usually
timely delivery of a certain number of packets is required.
Traditional timing analysis of WSNs are, however, either
focused on individual packets or traffic flows from individual
nodes a spatio-temporal fluid model is developed to capture
the delay characteristics of event detection in large-scale
WSNs. Mean delay and soft delay bounds are analyzed for
different network parameters. The resulting framework can be
utilized to analyze the effects of network and protocol
parameters on event detection delay to realize real-time
operation in WSNs. but fail to give single hop delay
distribution.
2. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 7, July 2013
2312
www.ijarcet.org
Yunbo Wang, Mehmet C. Vuran and Steve Goddard
have proposed that Limited energy resources in
increasingly sophisticated wireless sensor
networks(WSNs) call for a comprehensive crosslayer
analysis of energy consumption in a multi-hop network.
reliability analysis in such networks, the statistical
information about energy consumption and lifetime is
required Traditional energy analysis approaches only
focus on the average energy consumed. a stochastic
analysis of the energy consumption in a random network
environment. the distribution of energy consumption for
nodes in WSNs during a given time period is found. Fail
to analyze the energy consumption for more MAC
protocols, such as BMAC , XMAC , using our model.
Mehmet C. Vuran, Member, IEEE, and Ian F. Akyildiz,
Fellow, proposed that[3] Severe energy constraints of
battery-powered sensor nodes necessitate energy-
efficient communication in Wireless Sensor Networks
(WSNs). the vast majority of the existing solutions are
based on the classical layered protocol approach, which
leads to significant overhead a cross-layer protocol
(XLP) is introduced, which achieves congestion control
routing, and medium access control in a cross-layer
fashion.
The design principle of XLP is based on the
cross-layer concept of initiative determination, which
enables receiver-based contention, initiative-based
forwarding, local congestion control, and distributed
duty cycle operation to realize efficient and reliable
communication in WSN .Fail to investigate of various
networking functionalities such as adaptive modulation
,error control, and topology control in a cross-layer
fashion to develop a unified cross-layer communication
module Omesh Tickoo and Biplab Sikdar[2] proposed
that Traditional system fail to evaluating the queueing
delays and channel access times at nodes in wireless
networks paper presents an analytic model for evaluating
the queueing delays and channel access times at nodes in
wireless networks using the IEEE 802.11 Distributed
Coordination Function (DCF) as the MAC protocol. The
model can account for arbitrary arrival patterns, packet
size distributions and Number of nodes. Fail to give end
to end delay analysis for deterministic and random
deployment of nodes in WSN.
III. System Overview
Wireless sensor networks (WSNs) have been utilized in
many applications as both a connectivity infrastructure
and a distributed data generation network due to their
ubiquitous and flexible nature . Increasingly, a large
number of WSN application requires real-time quality-
of-service (QoS) guarantees. Such QoS requirements
usually depend on two common parameters: timing and
reliability. The resource constraints of WSNs, however,
limit the extent to which these requirements can be
guaranteed. Furthermore, the random effects of the
wireless channel prohibits the development of
deterministic QoS guarantees in these multihop
networks. Consequently, a probabilistic analysis of QoS
metrics is essential to address both timing and reliability
requirements.
In our analysis, we consider a network
composed of sensor nodes that are distributed in a 2-D
field.Sensor nodes report their readings to a sink through
a multihop route in the network.Two different types of
network deployments are investigated.Figure.1shows
the architectural diagram of our cross layer framework
Network deployment is divided into two types
1.Determnistic deployment:-The deterministic
deployment has the position of sensor nodes is fixed
with deterministic locations which is useful to calculate
the single hop delay distribution with queuing model.
2.Random Deployment:-Random deployment uses
Poisson point process with log normal fading channel.
queuing model deals with inter arrival distribution and
discrete time Markov Process. Locally generated packets
gives the local packet information. End to end delay is
calculated by the sum of incoming relay traffic rate at
each of the next hop.[1] Figure.2 shows the activity
diagram for developing the cross layer framework.
Figure.3 shows the structure of Markov chain showing
successful transmission of the packets with 3 attempts.
As shown in figure successfully transmitted traffic rate
from one node should be equal to the sum of the
incoming relay traffic rate at each of the next-hop
neighbors of the node.The topology of the queueing
network depends on the routing protocol used which is
also elaborated in activity diagram also. The discrete
time Markov chain is made up of M+1 layers , where
each layer m(0<m<M) represents the state there are m
packets in the queue and M is the queue capacity. The
detailed knowledge of Discrete chain Markov Process is
essential while developing a cross layer. Before each
transmission , the packet in the queue is transferred from
the microcontroller to transreceiver. The time needed for
such transfer differs for various transreceivers, but it is
not negligible. Our Experiments with Network Simulator
2 suggest that the durations of loading time and after
radio transmission are constant and approximately 1.7
and 2.0 ms, respectively.
3. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 7, July 2013
2313
www.ijarcet.org
Figure 1: Architecture Diagram
Figure 2: Activity Diagram
End-to-End Delay Distribution:-
Figure 3: Structures of Markov chains are shown in (a) for
{xn} and (b) for {Yn}. The common structure of blocks{zn }
and{In} are shown in (c) and (d), respectively.[1]
With each hop modeled as a Geom/PH/1/M queue, the entire
network is considered as a queuing network. Nodes are
interrelated according to the traffic constraints. More
specifically, the successfully transmitted traffic rate from one
node should be equal to the sum of the incoming relay traffic
rate at each of the next-hop neighbors of the node. The
topology of the queuing network depends on the routing
protocol used. In this paper, we focus on the class of routing
protocols with which each node maintains a probabilistic
routing table for its neighbors, e.g., geographic routing
protocols [4].Nodes relay their packets to each of their
neighbors according to a probability in their routing tables. By
first calculating the relaying traffic and the single hop delay
distribution for each pair of nodes, the end-to-end delay is
obtained using an iterative procedure.[1]
A. Constructing Markov Chain{Xn }
The discrete time Markov chain
{Xn} is made up of M+1 layers , where each layer m(0<m<M)
represents the state there are m packets in the queue and M is
the queue capacity. idle layer {In}and the communication
layers{Cn}m ,each of which consists of one or more states
each of which consists of one or more states. The states and
4. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
Volume 2, Issue 7, July 2013
2314
www.ijarcet.org
the transitions among the states in each layer are determined
by the protocols used by each node and represent the
operations conducted by the nodes according to the protocols.
The idle layer{In}(m=0),represents the idle process, during
which the node does not have any packet to send and waits for
new packets. The communication layers {Cn}m, (m>0)
represent the communication process in which packets are
transmitted
According to the MAC protocol
employed, and are respectively parameterized by the
following notations:
•PI and PC : the transition probability matrix among the states
in {In }and {Cn} ;
•αI and αC : the initial probability vector for {In}and
{Cn};
• tI
S
and tc
s
: the probability vector from each state in{In} and
{Cn} to complete the idle process and the transmission process
successfully;
•tc
f
: the probability vector from each state in to complete the
transmission process unsuccessfully;
•ג 1 and ג 2: the packet arrival probability vector for each state
in{In}and {Cn} . Each element in the vector is the probability
of a new packet arrival in a time unit when the process is in
the corresponding state.
Each communication layer {Cn }consists of Markov chain
blocks for each transmission attempt {Zn} , which is further
characterized by the transition probability matrix Pz , the
initial probability vector αz, the success probability vector tz
s
,
the failure probability vector tz
f
, and packet arrival probability
vector גz.
Accordingly, the transition probability matrix among the
states in a single layer {Cn} in {Xn} can be organized as rows
and columns of blocks
where the number of PZ blocks in PC is equal to x , i.e., the
maximum number of attempts for each packet transmission.
Similarly, the initial probability vector αc and the probability
vectors tc
s
and tc
f
to complete a layer in success and failure are
respectively organized as
αC = [ αZ 0 ……. 0 ] (1)
tc
s
= [tz
s
tz
s ………
tz
s
]T
(2)
tc
f
= [0 0 ……tz
f
]T
(3)
Note that since the idle layer does not have multiple attempts
like the communication layer does, there is no similar
organized internal pattern in the corresponding matrices and
vectors for {In}. The states and the transitions related to {In}
and {Zn} depend on the MAC protocol employed. The
transition probability matrix Qx of the entire Markov chain
{Xn} can then be found according to transitions between
different states at each layer as explained next.[1]
For layer m ,1<m<M-1 , the queue is not full.
Whenever a packet arrives, the process transits to a higher
layer since the queue length increases. The probabilities of
such transitions are governed by the probability matrix
Au=(1 גc )T
* Pc (4)
where is a properly dimensioned matrix containing all 1’s, and
* is the entrywise product operator.גc and Pc are parameterized
according to the MAC protocol. Note that element (v,v’) in Au
represents the transition probability from the v th state in
previous layer to the v’ th state in the upper layer, and other
transition probability matrices in the following are defined the
similar way. The transition probability matrix at the same
level m ,1<m<M-1, is
As=(1גc)T
*(tcαc) + (1-1גc)T
*Pc (5)
Where tc = tc
s
+ tc
f
is the probability vector from each layer to
complete the current communication process regardless of
success or failure. The first term in (5) captures the case where
a locally generated packet arrives at the same time unit in
which a packet service is completed. The second term in (5) is
for the case where neither service completion nor new packet
arrival occurs during the time unit.
At layer m=M, the queue is full. Hence,
new arriving packets are directly dropped. Therefore, the
transition probability matrix in this layer is Au + As .When
there is no packet arrival and the current packet service is
completed, the Markov chain transits to one layer below.The
transition probability matrix from level m+1 to level m
,1<m<M-1 is
Ad = (1-1גc)T *
tcαc (6)
The transition probabilities are similar when the idle layer is
involved as follows:
Au0= גI
T
* αC ( 7)
Ad0=( 1-1גc)T
*tcαI (8)
As0 = ( 1-1גI)T
* (PI + tI
S
αI (9)
When a new packet arrives while there
is no packet in the system, the chain transits from the idle
layer to layer 1 according TO Au0 to in (8). When the service
is completed for the only packet in the system and no new
packet arrives, the chain transits from layer 1 to the idle layer
according to Ad0 in(8).
Finally, the transition probabilities with
which the node stays in the idle layer are given in As0 in (9).
Using (4)–(9), the transition probability matrix Qxfor
the entire recurrent Markov chain {Xn} can be constructed as
follows:
Qx
Event Scheduler
To drive the execution of the simulation, to
process and schedule simulation events, NS makes use of
the concept of discrete event schedulers . In NS, network
components that simulate packet-handling delay or that
need timers use event schedulers. Figure 4 shows two
network objects, each of it using an event scheduler. If an
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network object issues an event, it has also to handle the
event later at scheduled time.
In NS, there are two different types of event schedulers –
real-time and non-real-time schedulers. There are three
implementations (List, Heap and Calendar) for non-real-
time schedulers; the default is Calendar.
Figure.4 The discrete event scheduler.[11]
Hardware Emulation
The real time scheduler (one of the two types of NS event
schedulers) is used for emulation. Emulation allow the
simulator to interact with a real live network NS is an OTcl
script interpreter with network simulation object libraries. But
NS in not only written in OTcl but also in C++. For efficiency
reasons, NS exploits a split-programming model. This is
because the developers of NS have found that separating the
data path implementation from the control path
implementation will reduce packet and event processing time.
Task such as low-level event processing and packet
forwarding requires high performance and are modified
infrequently, therefore the event scheduler and the basic
network component objects in the data path are implemented
in a compiled language that is C++. [11]
NS2 provides users with an
executable command ns which takes on input argument, the
name of a Tcl simulation scripting file. Users are feeding the
name of a Tcl simulation script (which sets up a simulation) as
an input argument of an NS2 executable command ns. In most
cases, a simulation trace file is created, and is used to plot
graph and/or to create animation.NS2 consists of two key
languages: C++ and Object-oriented Tool Command
Language (OTcl). While the C++ defines the internal
mechanism (i.e.,a backend) of the simulation objects, the OTcl
sets up simulation by assembling and configuring the objects
as well as scheduling discrete events (i.e., a frontend). The
C++ and the OTcl are linked together using TclCL. Mapped to
a C++ object, variables in the OTcl domains are sometimes
referred to as handles.
Conceptually, a handle (e.g., n as a
Node handle) is just a string (e.g.,_o10) in the OTcl domain,
and does not contain any functionality. Instead, the
functionality (e.g., receiving a packet) is defined in the
mapped C++ object (e.g., of class Connector). In the OTcl
domain, a handle acts as a frontend which interacts with users
and other OTcl objects. It may defines its own procedures and
variables to facilitate the interaction. Note that the member
procedures and variables in the OTcl domain are called
instance procedures instprocs) and instance variables
(instvars), respectively. Before proceeding further, the readers
are encouraged to learn C++ and OTcl languages. We refer the
readers to for the detail of C++, while a brief tutorial of Tcl
and OTcl tutorial are given in Appendices A.1 and A.2,
respectively. NS2 provides a large number of built-in C++
objects. It is advisable to use these C++ objects to set up a
simulation using a Tcl simulation script. However, advance
users may find these objects insufficient. They need to
develop their own C++ objects, and use a OTcl configuration
interface to put together these objects. After simulation, NS2
outputs either text-based or animation-based simulation
results. To interpret these results graphically and interactively,
tools such as NAM (Network AniMator) and XGraph are
used. To analyze a particular behavior of the network, users
can extract a relevant subset of text-based data and transform
it to a more conceivable presentation.[11]
Installation
NS2 is a free simulation tool, which can be
obtained from . It runs on various platforms including UNIX
(or Linux), Windows, and Mac systems.Being developed in
the Unix environment, with no surprise, NS2 has the
smoothest ride there, and so does its installation. Unless
otherwise specified,the discussion in this book is based on a
Cygwin (UNIX emulator) activated Windows system. NS2
source codes are distributed in two forms: the all-in-one suite
and the component-wise. With the all-in-one package, users
get all the required components along with some optional
components. [11]
The current all-in-one suite consists of the following main
components:
• NS release 2.30,
• Tcl/Tk release 8.4.13,
• OTcl release 1.12, and
• TclCL release 1.18.
and the following are the optional components:
• NAM release 1.12: NAM is an animation tool for viewing
network simulation traces and packet traces.
• Zlib version 1.2.3: This is the required library for NAM.
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IV. Discussion and results
Figure 3(a): CSMA/CA Mac Protocol
Figure 3(b): Average and end to end delay for CSMA/CA
Mac protocol.
Figure 4(a): Graph of Throughput Vs Packetloss
Crosslayer protocol.
Figure 5(a): Comparison graph of Throughput Vs
Packetloss Crosslayer and CSMA/CA Mac
Figure 3(b): Graph of Throughput Vs Packetloss For
CSMA/CA Mac protocol.
Figure 4(a): Crosslayer protocol
Figure 3(b): Average and end to end delay for Crosslayer
protocol.
Figure 5(b): Comparison graph of End to end delay
Crosslayer and CSMA/CA Mac
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The end-to-end delay distribution model has
been evaluated using NS2 to determine the single-hop and
multi hop delay distributions for the CSMA/CA MAC
protocol and the cross layer protocol . The computing
environment is a PC with a INTEL I3 working at 2.66
GHz and 4 GB RAM. Moreover, empirical experiments
and NS2-based simulations have been conducted on our
WSN test bed to validate the results. The simulations are
conducted in the same PC environment. For the empirical
validations, The packet size is B. Each node generates
local traffic to be sent to sink according to a Poisson
distribution with rate . Our experiments with the NS2
suggest that it requires on the average 1.7 ms to transfer
each packet from the sender to the receiver. The
transmission power is set to 15 dBm for all the
experiments unless otherwise stated. In the experiments,
the single-hop delay and end-to-end delay are measured as
follows. When the source node generates a packet, it
simultaneously sends an electric pulse to the destination
node through a pair of wires. The destination node starts a
timer when it receives the pulse and waits for the packet.
When the packet is received by the destination node, the
duration after the reception of the pulse is recorded as the
packet delay. This eliminates the need for synchronization
among all the nodes.
As shown in fig.3(a) a wireless topology
of nodes is implemented with CSMA/CA Mac protocol. A
packet transfer from sender to receiver is shown with the
shortest path possible with the CSMA/CA Mac protocol.
Algorithm finds the nearest node for transmitting the
packet in network. As shown in Fig.3(b) the graph of
throughput vs packetloss is drawn for CSMA/CA protocol.
Graph clearly shows that as packet loss is increased the
throughput is decreased. Fig.3(c) shows the average delay
for sending packet from sender to receiver as shown in the
network topology of module 2. Fig. also shows the average
end to end delay for sending packet from one end of
network to the other end, for CSMA/CA Mac
protocol.Fig.4(a) shows the network topology for Cross
layer protocol Fig. shows the packet transfer from sender
to receiver Sender nodes are shown by green and red color
while receiver nodes are shown by pink color. The average
delay for sending packet from sender to receiver is
calculated for cross layer protocol. Also average end to
end delay for sending packet from one end to other end of
network is calculated for cross layer protocol. As shown
in Fig.4(b) the graph of throughput vs packet loss is drawn
for Cross layer protocol. Graph clearly shows that as
packet loss is increased the throughput is decreased.
Fig.4(c) shows the average delay for sending packet from
sender to receiver as shown in the network topology of
module 2. Fig. also shows the average end to end delay for
sending packet from one end of network to the other end,
for Cross layer Mac protocol.
fig.5(a) shows the comparison of
CSMA and cross layer protocol for throughput vs packet
loss. as the red line indicates cross layer protocol and green
line shows the CSMA/CA Mac protocol. the graph clearly
shows that the packet loss is less in cross layer protocol as
compared to CSMA/CA protocol. due to this throughput
is more for cross layer protocol as compared to CSMA
/CA Mac protocol.
The average delay for the CSMA/CA
protocol is 0.267msec considering transfer of 30 packets.
The End to end delay for CSMA/CA Mac protocol is
0.5112 msec. The average delay for Cross layer protocol
is 0.062 m sec considering transfer of 30 packets. The end
to end delay for Cross layer protocol is 0.11008 msec.
Following Table I shows the analytical results.
Table I
Sr.
No
.
Parameter No.of
Packets
CSMA Crosslayer
1. Average
Delay(msec)
30 0.267 0.5112
2. End-to-End
Delay(msec)
30 0.062 0.11008
3. Average
Delay(msec)
80 0.512 0.9843
4. End-to-End
Delay(msec)
80 0.342 0.29870
V. Conclusion and Future work
In this paper, an end-to-end analysis of the
communication delay is provided. Our model shows
comparatively stronger results for Cross layer protocol
than CSMA/CA Mac protocol as shown in Table I. A
Markov process is used to model the communication
process in network. Average and End to end delay for
CSMA/CA protocol and Cross layer protocol is calculated.
The results show that the developed framework accurately
models the distribution of the end-to-end delay and
captures the heterogeneous effects of multi hop WSNs.
The developed framework can be used to find out the
Throughput and packet loss for the both CSMA/CA Mac
and Cross layer protocol. for WSNs
In some applications, the traffic generated for the
physical event can be bur sty. For tractability, the bur sty
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traffic pattern is not considered in this project. so in future
we can implement a system with bursty traffic. As future
work, we plan to analyze the delay for more MAC
protocols, such as BMAC [21], XMAC [3], using our
model. We also plan to extend the model to capture more
generic network topologies, and traffic types, such as
periodic and bursty traffics. Moreover, other network
lifetime definitions will be investigated. We also plan
extend our model to proposals in IEEE 802.11e to reduce
these delays which allow a node to schedule a burst of
packets once they gainchannel access. Each node in now
modeled as a discrete time queue with interruptions.
VI. References
[1] Yunbo Wang, Member, IEEE, Mehmet C. Vuran,
Member, IEEE, and Steve Goddard, Member, IEEE
“Cross-Layer Analysis of the End-to-End Delay
Distribution in Wireless Sensor Networks.” IEEE/ACM
transactions on networking, vol. 20, no. 1, february 2012
[2] Omesh Tickoo and Biplab Sikdar, Member, IEEE
“Modeling Queueing and Channel Access Delay in
Unsaturated IEEE 802.11 Random Access MAC Based
Wireless Networks.” IEEE/ACM transactions on
networking, vol. 16, no. 4, august 2008
[3] “Stochastic Analysis of Energy Consumption in
Wireless Sensor Networks.” Yunbo Wang, Mehmet C.
Vuran and Steve Goddard Department of Computer
Science and Engineering,University of Nebraska-Lincoln
[4] T. Abdelzaher, S. Prabh, and R. Kiran, “On real-time
capacity limits of multihop wireless sensor networks,” in
Proc. IEEE RTSS, Lisbon, Portugal, Dec. 2004, pp. 359–
370.
[5] K. Akkaya and M. Younis, “A survey on routing
protocols for wirelesssensor networks,” Ad Hoc Netw., vol.
3, no. 3, pp. 325–349, Sep. 2005.
[6] I. F. Akyildiz, T. Melodia, and K. R. Chowdhury, “A
survey on wireless multimedia sensor networks,” Comput.
Netw. J., vol. 51, no. 4, pp. 921–960, Mar. 2007.
[7] I. F. Akyildiz,W. Su, Y. Sankarasubramaniam, and E.
Cayirci, “Wireless sensor networks: A survey,” Comput.
Netw. J., vol. 38, no. 4, pp. 393–422, Mar. 2002.
[8] G. Bianchi, “Performance analysis of the IEEE 802.11
distributed coordination function,” IEEE J. Sel. Areas
Commun., vol. 18, no. 3, pp.535–547, Mar. 2000.
[9] N. Bisnik and A. Abouzeid, “Queuing network models
for delay analysis of multihop wireless ad hoc networks,”
Ad Hoc Netw., vol. 7, no.1, pp. 79–97, Jan. 2009.
[10] A. Burchard, J. Liebeherr, and S. Patek, “A min-plus
calculus for end-to-end statistical service guarantees,”
IEEE Trans. Inf. Theory, vol. 52, no. 9, pp. 4105–4114,
Sep. 2006.
[11] Teerawat Issariyakul & EkramHossain”Introduction
to Network Simulator 2.”2009 Springer Science,Business
Media,
Ravindra J. Lawande 1
BE in Electronics from
Pune University,
pursuing ME in VLSI
and embedded form
PREC, loni, Pune
University, working as
an assistant professor at
PDVVPCOE,
Ahmednagar,
Maharashtra ,India
His field of interest is
Wireless Comm. and
Microwave.
.
Abdul .H.Ansari 2
BE and ME from
S.S.G.MCE,Shegaon,
Amaravati University
has 16 years of teaching
Experience, presently
working as Associate
professor at PREC,Loni
His field of interest is
Wireless Comm. and
Cognitive Radio.