Path constrained data gathering scheme for wireless sensor networks with mobi...ijwmn
Wireless Sensor Networks (WSNs) have emerged as a promising solution for variety of applications.
Recently, in order to increase the lifetime of the network, many proposals have introduced the use of
Mobile Elements (MEs) as a mechanical carrier to collect data. In this paper, we investigate the problem of
designing the mobile element tour to visit subset of the nodes, termed as caching points, where the length of
the mobile element tour is bounded by pre-determined length. Caching can be implemented at various
points on the network such that any node in the network is at most k-hops away from one of these caching
points. To address this problem, we present heuristic-based solution. Our solution works by partitioning
the network such that the depth of each partition is bounded by k. Then, in each partition, the minimum
number of required caching points is identified. We compare the resulting performance of our algorithm
with the best known comparable schemes in the literature.
Effect of mobility models on the performance of multipath routing protocol in...csandit
In this paper, we have analyzed the performance of multipath routing protocol with various mobility
models for Mobile Ad Hoc Networks. The basic purpose of any multipath routing protocol
is to overcome various problems occurs while data delivery through a single path routing protocol.
For high acceptability of routing protocol, analysis of routing protocol in ad hoc network
only with random way point mobility model is not sufficient. Here, we have considered Random
waypoint, Random Direction and Probabilistic Random Walk mobility Model for proper analysis
of AOMDV routing protocol. Results obtained show that with increasing node density, packet
delivery ratio increases but with increasing node mobility Packet delivery ratio decreases.
Effect of node mobility onaomdv protocol in manetijwmn
In this paper, we have analyzed the effect of node mobility on theperformance of AOMDV multipath routing
protocol. This routing protocol in ad hoc network has been analyzed with random way point mobility model
only. This is not sufficient to evaluate the behavior of a routing protocol. Therefore, in this paper, we
have considered Random waypoint, Random Direction and Probabilistic Random Walk mobility Model for
performance analysis of AOMDV protocol. The result reveals that packet delivery ratio decreases with the
increasing node mobility forall mobility models. Also, average end-to-end delay is also vary with varying
node speed, initially upto 20 nodes in all mobility models delay is minimum.
Computer network is becoming more popular and common, the need to use the broadband connection services (e-learning - online training, video conferencing - online conference, IPTV - digital TV ...) of organizations and individuals is increasing. Multicast is an effective mechanism for the transmission of information and data to many recipients simultaneously. Multicast is a routing problem from a source node to a receiver node set, also known as the routing from one point to multipoint. The advances in technology and multimedia applications emerge quickly has provided great motivation for the application of new real-time multi-point. Many multi-point applications will not function properly if the QoS (quality of service) can not be guaranteed. Therefore, multi-point algorithms must be able to meet the QoS constraints (cost, reliability, bandwidth, jitter, delay...). The objective of multicast routing algorithms guarantee QoS is to provide routing algorithms have the ability to recognize the tree to satisfy the maximum of traffic streams with QoS requirements. Most multicast algorithms on MPLS (MultiProtocol Label Switching) considered the unique QoS constraint as bandwidth. The other QoS constraints can be converted into bandwidth efficiency. Starting from this reality, this paper research multicast routing algorithms guarantee bandwidth and propose new algorithm compares with existing ones.
Performance analysis of aodv, olsr, grp and dsr routing protocols with databa...eSAT Journals
Abstract
Wireless Technology has an enormous use these days and is still becoming popular from times immemorial. It is at its peak when we
talk about research. This is because of the latest technological demands now days arising from Laptops, Wireless devices such as
Wireless local area networks (WLANs) etc. Because of its fast growing popularity day by day, it has led wireless technology data rates
higher and it has made its price cheaper, which is why wireless Technology is growing so fast. In this paper we have presented some
most commonly used routing protocols in MANET and compared the performance of AODV, OLSR, GRP and DSR routing protocol
by using OPNET simulator 14.5. The performance is evaluated under different parameters like Delay, Load, and Media access delay,
Network Load, Retransmission and Throughput for Database load.
Keywords— MANET, Peak Value, Protocol, Drop value
Comparative Study for Performance Analysis of Routing Protocols in Mobility a...eeiej
A Mobile Ad-hoc Network (MANET) has a collection of numbers of wireless nodes which is each device in
MANET having ability to free to move in any direction so that it is useful in all applications. In MANET
nodes change position quite frequently, this means that we have need routing protocols that quickly adapts
to topology changes. An ad-hoc network is self-organising and distributive in manner. The MANET is
works as router so that linked with the other nearest devices. A mobile ad hoc network (MANET) is a
wireless network follows the multiple hop routing instead of static network infra to provide network
connectivity. Each device in a MANET is free to move independently in all direction freewaysand will
therefore nodes change position in large networks all routing protocols. The routing protocols are needed
for conveying information in Ad-hoc network there are various performance parameters to compare the
Ad-hoc routing protocols.
My area of interest in this paper to analyze the different performance parameters Recent research has
focused on simulation studies with mobility and non- mobility scenarios to investigate and improve routing
protocols. we have simulate the all three routing protocols in mobility as well as non-mobility scenario
with nodes up to 300 also plot the graphs throughput, Delay, PDR, Dropping Ratio, and average energy
consumption by using Network Simulator version 2.34. ).
Path constrained data gathering scheme for wireless sensor networks with mobi...ijwmn
Wireless Sensor Networks (WSNs) have emerged as a promising solution for variety of applications.
Recently, in order to increase the lifetime of the network, many proposals have introduced the use of
Mobile Elements (MEs) as a mechanical carrier to collect data. In this paper, we investigate the problem of
designing the mobile element tour to visit subset of the nodes, termed as caching points, where the length of
the mobile element tour is bounded by pre-determined length. Caching can be implemented at various
points on the network such that any node in the network is at most k-hops away from one of these caching
points. To address this problem, we present heuristic-based solution. Our solution works by partitioning
the network such that the depth of each partition is bounded by k. Then, in each partition, the minimum
number of required caching points is identified. We compare the resulting performance of our algorithm
with the best known comparable schemes in the literature.
Effect of mobility models on the performance of multipath routing protocol in...csandit
In this paper, we have analyzed the performance of multipath routing protocol with various mobility
models for Mobile Ad Hoc Networks. The basic purpose of any multipath routing protocol
is to overcome various problems occurs while data delivery through a single path routing protocol.
For high acceptability of routing protocol, analysis of routing protocol in ad hoc network
only with random way point mobility model is not sufficient. Here, we have considered Random
waypoint, Random Direction and Probabilistic Random Walk mobility Model for proper analysis
of AOMDV routing protocol. Results obtained show that with increasing node density, packet
delivery ratio increases but with increasing node mobility Packet delivery ratio decreases.
Effect of node mobility onaomdv protocol in manetijwmn
In this paper, we have analyzed the effect of node mobility on theperformance of AOMDV multipath routing
protocol. This routing protocol in ad hoc network has been analyzed with random way point mobility model
only. This is not sufficient to evaluate the behavior of a routing protocol. Therefore, in this paper, we
have considered Random waypoint, Random Direction and Probabilistic Random Walk mobility Model for
performance analysis of AOMDV protocol. The result reveals that packet delivery ratio decreases with the
increasing node mobility forall mobility models. Also, average end-to-end delay is also vary with varying
node speed, initially upto 20 nodes in all mobility models delay is minimum.
Computer network is becoming more popular and common, the need to use the broadband connection services (e-learning - online training, video conferencing - online conference, IPTV - digital TV ...) of organizations and individuals is increasing. Multicast is an effective mechanism for the transmission of information and data to many recipients simultaneously. Multicast is a routing problem from a source node to a receiver node set, also known as the routing from one point to multipoint. The advances in technology and multimedia applications emerge quickly has provided great motivation for the application of new real-time multi-point. Many multi-point applications will not function properly if the QoS (quality of service) can not be guaranteed. Therefore, multi-point algorithms must be able to meet the QoS constraints (cost, reliability, bandwidth, jitter, delay...). The objective of multicast routing algorithms guarantee QoS is to provide routing algorithms have the ability to recognize the tree to satisfy the maximum of traffic streams with QoS requirements. Most multicast algorithms on MPLS (MultiProtocol Label Switching) considered the unique QoS constraint as bandwidth. The other QoS constraints can be converted into bandwidth efficiency. Starting from this reality, this paper research multicast routing algorithms guarantee bandwidth and propose new algorithm compares with existing ones.
Performance analysis of aodv, olsr, grp and dsr routing protocols with databa...eSAT Journals
Abstract
Wireless Technology has an enormous use these days and is still becoming popular from times immemorial. It is at its peak when we
talk about research. This is because of the latest technological demands now days arising from Laptops, Wireless devices such as
Wireless local area networks (WLANs) etc. Because of its fast growing popularity day by day, it has led wireless technology data rates
higher and it has made its price cheaper, which is why wireless Technology is growing so fast. In this paper we have presented some
most commonly used routing protocols in MANET and compared the performance of AODV, OLSR, GRP and DSR routing protocol
by using OPNET simulator 14.5. The performance is evaluated under different parameters like Delay, Load, and Media access delay,
Network Load, Retransmission and Throughput for Database load.
Keywords— MANET, Peak Value, Protocol, Drop value
Comparative Study for Performance Analysis of Routing Protocols in Mobility a...eeiej
A Mobile Ad-hoc Network (MANET) has a collection of numbers of wireless nodes which is each device in
MANET having ability to free to move in any direction so that it is useful in all applications. In MANET
nodes change position quite frequently, this means that we have need routing protocols that quickly adapts
to topology changes. An ad-hoc network is self-organising and distributive in manner. The MANET is
works as router so that linked with the other nearest devices. A mobile ad hoc network (MANET) is a
wireless network follows the multiple hop routing instead of static network infra to provide network
connectivity. Each device in a MANET is free to move independently in all direction freewaysand will
therefore nodes change position in large networks all routing protocols. The routing protocols are needed
for conveying information in Ad-hoc network there are various performance parameters to compare the
Ad-hoc routing protocols.
My area of interest in this paper to analyze the different performance parameters Recent research has
focused on simulation studies with mobility and non- mobility scenarios to investigate and improve routing
protocols. we have simulate the all three routing protocols in mobility as well as non-mobility scenario
with nodes up to 300 also plot the graphs throughput, Delay, PDR, Dropping Ratio, and average energy
consumption by using Network Simulator version 2.34. ).
Maximizing Throughput using Adaptive Routing Based on Reinforcement LearningEswar Publications
In this paper, prioritized sweeping confidence based dual reinforcement learning based adaptive routing is studied. Routing is an emerging research area in wireless networks and needs more research due to emerging technologies such as wireless sensor network, ad hoc networks and network on chip. In addition, mobile ad hoc network suffers from various network issues such as dynamicity, mobility, data packets delay, high dropping ratio, large routing overhead, less throughput and so on. Conventional routing protocols based on distance vector
or link state routing is not much suitable for mobile ad hoc network. All existing conventional routing protocols are based on shortest path routing, where the route having minimum number of hops is selected. Shortest path routing is non-adaptive routing algorithm that does not take care of traffic present on some popular routes of the network. In high traffic networks, route selection decision must be taken in real time and packets must be diverted on some alternate routes. In Prioritized sweeping method, optimization is carried out over confidence based dual reinforcement routing on mobile ad hoc network and path is selected based on the actual traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on 50 nodes MANET with random mobility and 50 nodes fixed grid network. Throughput is used to judge the performance of network. Analysis is done by varying the interval between the successive packets.
Mobile Relay Configuration in Data-Intensuive Wireless Sensor with Three Rout...IJERA Editor
Wireless sensor network are increasingly used in data-intensive applications such as micro-climate monitoring,
precision agriculture and audio/video surveillance. A key challenges faced by data-intensive wsn’s is to transmit
all the data generated with an application’s lifetime to the base station despite the fact that sensor nodes have
limited power supply. We propose using low-cost disposable mobile really and our work in the following
aspects First, it does not require complex motion planning of mobile nodes. Second we integrate the energy
consumption due to both mobility and wireless transmission. Our framework consists of first algorithm
computes an optimal routing tree. The second, we integrate the energy consumption due to both mobility and
wireless transmissions .The second algorithm improves the topology of the routing tree by greedily adding new
nodes. The third algorithm improves the routing tree by relocating its nodes without changing its topology.
Frequently forming a network topology without the use of any existing network infrastructure. We compare the
performance of the three prominent routing protocols for the mobile relay is Adhoc on Demand Distance Vector
(ADVO), Destination Sequenced Distance Vector (DSDV) and Temporally Ordered Routing Protocols (TORA).
We have chosen four performance metrics such as Average Delay, Packet Delivery Fraction, Routing load and
varying Mobility nodes, simulation for the popular routing protocols AODV, DSDV, and TORA. The
simulation is carried out on NS-2. The performance differentials are analyzed using varying network size and
simulations times. The simulation results confirm that ADVO performs well in terms of Average Delay, Packet
Delivery Fraction. As far as routing load concers TORA performs well.
Mobility is one of the basic features that define an ad hoc network, an asset that leaves the field
free for the nodes to move. The most important aspect of this kind of network turns into a great
disadvantage when it comes to commercial applications, take as an example: the automotive
networks that allow communication between a groups of vehicles. The ad hoc on-demand
distance vector (AODV) routing protocol, designed for mobile ad hoc networks, has two main
functions. First, it enables route establishment between a source and a destination node by
initiating a route discovery process. Second, it maintains the active routes, which means finding
alternative routes in a case of a link failure and deleting routes when they are no longer
desired. In a highly mobile network those are demanding tasks to be performed efficiently and
accurately. In this paper, we focused in the first point to enhance the local decision of each node
in the network by the quantification of the mobility of their neighbours. Quantification is made
around RSSI algorithm a well known distance estimation method.
Analysis of Random Based Mobility Model using TCP Traffic for AODV and DSDV M...ijsrd.com
In Mobile Ad hoc network (MANETS), no fixed infrastructure is available. Different wireless hosts are free to move from one location to another without any centralized administration, so, the topology changes rapidly or unpredictably. Every node operates as router as well as an end system. Routing in MANETs has been a challenging task ever since the wireless networks came into existence. The major reason for this is continues changes in network topology because of high degree of node mobility. The MANET routing protocols have mainly two classes: Proactive routing (or table-driven routing) protocols and Reactive routing (or on-demand routing) protocols. In this paper, we have analyzed various Random based mobility models: Random Waypoint model, Random Walk model, Random Direction model and Probabilistic Random Walk model using AODV and DSDV protocols in Network Simulator (NS 2.35). The performance comparison of MANET mobility models have been analyzed by varying number of nodes using traffic TCP. The comparative conclusions are drawn on the basis of various performance metrics such as: Routing Overhead (packets), Packet Delivery Fraction (%), Normalized Routing Load, Average End-to-End Delay (milliseconds) and Packet Loss (%).
A Review of Ad hoc on demand distance vector routing and proposed AR-AODVEditor IJMTER
Mobile Ad-hoc networks are a key in the evolution of wireless networks. In mobile
ad hoc networks, there is no centralized infrastructure to monitor or allocate the resources
used by the mobile nodes. The absence of any central coordinator makes the routing a
complex one compared to cellular networks. The Ad hoc On Demand Distance Vector
(AODV) routing algorithm is a routing protocol designed for ad hoc mobile devices. AODV
uses an on demand approach for finding routes .A class of routing protocols called ondemand protocols has recently found attention because of their low routing overhead. The ondemand protocols depend on query floods to discover routes whenever a new route is needed.
Such floods take up a substantial portion of network bandwidth. The routing in Mobile ad hoc
network is difficult and number of reactive routing protocols like AODV, DSR, and DSDV
has been implemented. In this paper, an attempt has been made to thoroughly study all
AODVs and a new AODV is proposed called AR-AODV
Jamming aware traffic allocation for multiple-path routing using portfolio se...Saad Bare
Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. We show that in multisource networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the network's ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in several scenarios.
Performance Evaluation of Gauss-Markov Mobility Model in Hybrid LTE-VANET Net...TELKOMNIKA JOURNAL
Vehicular Ad-hoc Network (VANET) is developed based on mobile ad-hoc networks (MANET).
VANET has different characteristics than MANET. On VANET, a mobile node (MN) moves faster, topology
changes dynamically. The previous research shows that the model of mobility affects to the network
performance. In this paper, a Gauss-Markov mobility model is used to illustrate the motion of the MN. This
paper enriches the evaluation of the performance of Gauss-Markov mobility model on LTE-VANET hybrid
network, by evaluating various network performance metrics, i.e. packet delivery ratio (PDR), throughput,
and delay. This research simulates the Gauss-Markov mobility model with various numbers of nodes and
randomness index (α), using Network Simulator-3 (NS-3). The result shows that strong correlation among
PDR, throughput, and delay with the addition number of MNs. Based on the simulation result, the hybrid
LTE-VANET have smaller 40% average delay than the existing VANET. This simulation also concludes
that different value of alpha on Gauss-Markov mobility model does not influence PDR, throughput, and
delay.
CROSS LAYER DESIGN APPROACH FOR EFFICIENT DATA DELIVERY BASED ON IEEE 802.11P...pijans
Intelligent Transportation Systems (ITS) have been one of the promising technology that has a great
interest attention from many researchers over the world. Vehicular Ad-hoc Network (VANET)
communications environment as a part of ITS opens the way for a wide range of applications such as safety
applications, mobility and connectivity for both driver and passengers to exploit the transport systems in a
smoothly, efficiently and safer way. Several challenging tasks facing adopting VANET functionality for ITS
such as modelling of wireless transmission and routing issues. These research issues have become more
critical due to the high mobility of vehicles nodes (transmitters and receivers) and unexpected network
topology due to the high speed of nodes. In fact, modelling radio propagation channel in VANET
environment which considers as one of a stringent communications environment is a challenging task. The
selection of a suitable transmission model plays a key role in the routing decisions for VANET. Different
propagation models allow calculating the Received Signal Strength (RSS) based on key environmental
properties such as the distance between transmitter vehicle and a receiver vehicle, the gain and antenna
height of transmitter and a receiver vehicles. Hence, it is useful to calculate RSS and SNR values for a
specific propagation model and then these values can be used later for routing decision in order to find the
best path with high SNR. This paper evaluates the performance of different transmission models (free-
space, two-ray and log-normal) in terms of Receive Signal Strength (RSS). In addition, the performance of
such wireless transmission models for vehicular communication in terms of PDR, throughput and delay is
evaluated by applying the proposed cross layer routing approach based on IEEE 802.11p. By using
MATLAB, the obtained results confirm the best packet delivery ratio for our proposed approach, where it
indicates poor quality of DSSS PHY with high number vehicles. The minimum delay achieved when traffic
density is decreased
Performance comparison of mobile ad hoc network routing protocolsIJCNCJournal
Mobile Ad-hoc Network (MANET) is an infrastructure less and decentralized network which need a robust
dynamic routing protocol. Many routing protocols for such networks have been proposed so far to find
optimized routes from source to the destination and prominent among them are Dynamic Source Routing
(DSR), Ad-hoc On Demand Distance Vector (AODV), and Destination-Sequenced Distance Vector (DSDV)
routing protocols. The performance comparison of these protocols should be considered as the primary
step towards the invention of a new routing protocol. This paper presents a performance comparison of
proactive and reactive routing protocols DSDV, AODV and DSR based on QoS metrics (packet delivery
ratio, average end-to-end delay, throughput, jitter), normalized routing overhead and normalized MAC
overhead by using the NS-2 simulator. The performance comparison is conducted by varying mobility
speed, number of nodes and data rate. The comparison results show that AODV performs optimally well
not the best among all the studied protocols.
CROSS LAYER DESIGN APPROACH FOR EFFICIENT DATA DELIVERY BASED ON IEEE 802.11P...pijans
Intelligent Transportation Systems (ITS) have been one of the promising technology that has a great interest attention from many researchers over the world. Vehicular Ad-hoc Network (VANET) communications environment as a part of ITS opens the way for a wide range of applications such as safety applications, mobility and connectivity for both driver and passengers to exploit the transport systems in a smoothly, efficiently and safer way. Several challenging tasks facing adopting VANET functionality for ITS such as modelling of wireless transmission and routing issues. These research issues have become more critical due to the high mobility of vehicles nodes (transmitters and receivers) and unexpected network topology due to the high speed of nodes. In fact, modelling radio propagation channel in VANET environment which considers as one of a stringent communications environment is a challenging task. The selection of a suitable transmission model plays a key role in the routing decisions for VANET. Different propagation models allow calculating the Received Signal Strength (RSS) based on key environmental properties such as the distance between transmitter vehicle and a receiver vehicle, the gain and antenna height of transmitter and a receiver vehicles. Hence, it is useful to calculate RSS and SNR values for a specific propagation model and then these values can be used later for routing decision in order to find the best path with high SNR. This paper evaluates the performance of different transmission models (freespace, two-ray and log-normal) in terms of Receive Signal Strength (RSS). In addition, the performance of such wireless transmission models for vehicular communication in terms of PDR, throughput and delay is evaluated by applying the proposed cross layer routing approach based on IEEE 802.11p. By using MATLAB, the obtained results confirm the best packet delivery ratio for our proposed approach, where it indicates poor quality of DSSS PHY with high number vehicles. The minimum delay achieved when traffic density is decreased.
International Journal of Computational Engineering Research (IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Performance comparison of aodv and olsr using 802.11 a and dsrc (802.11p) pro...IJCNCJournal
A Vehicular Ad Hoc Network (VANET) is a network formed purely among vehicles without presence of any
communication infrastructure as base stations and/or access point. Frequent topological changes due to
high mobility is one of the main issues in VANETs. In this paper we evaluate Ad-hoc On-Demand Distance
Vector (AODV) and Optimized Link State Routing (OLSR) routing protocols using 802.11a and 802.11p in
a realistic urban scenario. For this comparison, we chose five performance metrics: Path Availability, Endto-
End Delay, Number of Created Paths, Path Length and Path Duration. Simulation results show, that for
most of the metrics evaluated, OLSR outperforms AODV when 802.11p and that 802.11p is more efficient
in urban VANETs.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Maximizing Throughput using Adaptive Routing Based on Reinforcement LearningEswar Publications
In this paper, prioritized sweeping confidence based dual reinforcement learning based adaptive routing is studied. Routing is an emerging research area in wireless networks and needs more research due to emerging technologies such as wireless sensor network, ad hoc networks and network on chip. In addition, mobile ad hoc network suffers from various network issues such as dynamicity, mobility, data packets delay, high dropping ratio, large routing overhead, less throughput and so on. Conventional routing protocols based on distance vector
or link state routing is not much suitable for mobile ad hoc network. All existing conventional routing protocols are based on shortest path routing, where the route having minimum number of hops is selected. Shortest path routing is non-adaptive routing algorithm that does not take care of traffic present on some popular routes of the network. In high traffic networks, route selection decision must be taken in real time and packets must be diverted on some alternate routes. In Prioritized sweeping method, optimization is carried out over confidence based dual reinforcement routing on mobile ad hoc network and path is selected based on the actual traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on 50 nodes MANET with random mobility and 50 nodes fixed grid network. Throughput is used to judge the performance of network. Analysis is done by varying the interval between the successive packets.
Mobile Relay Configuration in Data-Intensuive Wireless Sensor with Three Rout...IJERA Editor
Wireless sensor network are increasingly used in data-intensive applications such as micro-climate monitoring,
precision agriculture and audio/video surveillance. A key challenges faced by data-intensive wsn’s is to transmit
all the data generated with an application’s lifetime to the base station despite the fact that sensor nodes have
limited power supply. We propose using low-cost disposable mobile really and our work in the following
aspects First, it does not require complex motion planning of mobile nodes. Second we integrate the energy
consumption due to both mobility and wireless transmission. Our framework consists of first algorithm
computes an optimal routing tree. The second, we integrate the energy consumption due to both mobility and
wireless transmissions .The second algorithm improves the topology of the routing tree by greedily adding new
nodes. The third algorithm improves the routing tree by relocating its nodes without changing its topology.
Frequently forming a network topology without the use of any existing network infrastructure. We compare the
performance of the three prominent routing protocols for the mobile relay is Adhoc on Demand Distance Vector
(ADVO), Destination Sequenced Distance Vector (DSDV) and Temporally Ordered Routing Protocols (TORA).
We have chosen four performance metrics such as Average Delay, Packet Delivery Fraction, Routing load and
varying Mobility nodes, simulation for the popular routing protocols AODV, DSDV, and TORA. The
simulation is carried out on NS-2. The performance differentials are analyzed using varying network size and
simulations times. The simulation results confirm that ADVO performs well in terms of Average Delay, Packet
Delivery Fraction. As far as routing load concers TORA performs well.
Mobility is one of the basic features that define an ad hoc network, an asset that leaves the field
free for the nodes to move. The most important aspect of this kind of network turns into a great
disadvantage when it comes to commercial applications, take as an example: the automotive
networks that allow communication between a groups of vehicles. The ad hoc on-demand
distance vector (AODV) routing protocol, designed for mobile ad hoc networks, has two main
functions. First, it enables route establishment between a source and a destination node by
initiating a route discovery process. Second, it maintains the active routes, which means finding
alternative routes in a case of a link failure and deleting routes when they are no longer
desired. In a highly mobile network those are demanding tasks to be performed efficiently and
accurately. In this paper, we focused in the first point to enhance the local decision of each node
in the network by the quantification of the mobility of their neighbours. Quantification is made
around RSSI algorithm a well known distance estimation method.
Analysis of Random Based Mobility Model using TCP Traffic for AODV and DSDV M...ijsrd.com
In Mobile Ad hoc network (MANETS), no fixed infrastructure is available. Different wireless hosts are free to move from one location to another without any centralized administration, so, the topology changes rapidly or unpredictably. Every node operates as router as well as an end system. Routing in MANETs has been a challenging task ever since the wireless networks came into existence. The major reason for this is continues changes in network topology because of high degree of node mobility. The MANET routing protocols have mainly two classes: Proactive routing (or table-driven routing) protocols and Reactive routing (or on-demand routing) protocols. In this paper, we have analyzed various Random based mobility models: Random Waypoint model, Random Walk model, Random Direction model and Probabilistic Random Walk model using AODV and DSDV protocols in Network Simulator (NS 2.35). The performance comparison of MANET mobility models have been analyzed by varying number of nodes using traffic TCP. The comparative conclusions are drawn on the basis of various performance metrics such as: Routing Overhead (packets), Packet Delivery Fraction (%), Normalized Routing Load, Average End-to-End Delay (milliseconds) and Packet Loss (%).
A Review of Ad hoc on demand distance vector routing and proposed AR-AODVEditor IJMTER
Mobile Ad-hoc networks are a key in the evolution of wireless networks. In mobile
ad hoc networks, there is no centralized infrastructure to monitor or allocate the resources
used by the mobile nodes. The absence of any central coordinator makes the routing a
complex one compared to cellular networks. The Ad hoc On Demand Distance Vector
(AODV) routing algorithm is a routing protocol designed for ad hoc mobile devices. AODV
uses an on demand approach for finding routes .A class of routing protocols called ondemand protocols has recently found attention because of their low routing overhead. The ondemand protocols depend on query floods to discover routes whenever a new route is needed.
Such floods take up a substantial portion of network bandwidth. The routing in Mobile ad hoc
network is difficult and number of reactive routing protocols like AODV, DSR, and DSDV
has been implemented. In this paper, an attempt has been made to thoroughly study all
AODVs and a new AODV is proposed called AR-AODV
Jamming aware traffic allocation for multiple-path routing using portfolio se...Saad Bare
Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. We show that in multisource networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the network's ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in several scenarios.
Performance Evaluation of Gauss-Markov Mobility Model in Hybrid LTE-VANET Net...TELKOMNIKA JOURNAL
Vehicular Ad-hoc Network (VANET) is developed based on mobile ad-hoc networks (MANET).
VANET has different characteristics than MANET. On VANET, a mobile node (MN) moves faster, topology
changes dynamically. The previous research shows that the model of mobility affects to the network
performance. In this paper, a Gauss-Markov mobility model is used to illustrate the motion of the MN. This
paper enriches the evaluation of the performance of Gauss-Markov mobility model on LTE-VANET hybrid
network, by evaluating various network performance metrics, i.e. packet delivery ratio (PDR), throughput,
and delay. This research simulates the Gauss-Markov mobility model with various numbers of nodes and
randomness index (α), using Network Simulator-3 (NS-3). The result shows that strong correlation among
PDR, throughput, and delay with the addition number of MNs. Based on the simulation result, the hybrid
LTE-VANET have smaller 40% average delay than the existing VANET. This simulation also concludes
that different value of alpha on Gauss-Markov mobility model does not influence PDR, throughput, and
delay.
CROSS LAYER DESIGN APPROACH FOR EFFICIENT DATA DELIVERY BASED ON IEEE 802.11P...pijans
Intelligent Transportation Systems (ITS) have been one of the promising technology that has a great
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1. Obstruction Avoidance Generously
Mobility (OAGM) a new Obstacle
Mobility Model Based on Graph-Theory
17-Apr-2014 V.Vasanthi-10JLDRCS002 1
Research Scholar:
V.Vasanthi
10JLDRCS002
Dept. Computer Science
Karpagam University
Research Guide:
Dr. M. Hemalatha
Prof. Dept. Computer
Science
Karpagam University
2. Introduction
Aim and Objectives
Background(Literature Review)
Methodology
Results and Discussions
Conclusion
Future work
References
17-Apr-2014V.Vasanthi-10JLDRCS002 2
3. Ad-hoc and Sensor Network
• It is a self-configuring of nodes connected by wireless
link
• It forms an arbitrary topology
• It is distributed sensing and processing in wide range of
applications
• It consists of new concepts and optimization problems
openly
Mobility Model
• plays a vital role in movement
• dictates to the nodes their initial places and movement
patterns
• emulate real-life Scenarios
17-Apr-2014V.Vasanthi-10JLDRCS002 3
4. Contd..
Aspects of Mobility Models
User friendly
Sufficient and easy to understand
Mathematical properties
Scope and Validity
Realistic model(i.e)
It is not restricted in pre-defined pathways. The movement
pattern of the nodes in a natural way. The types of
Environments such as Urban, Social, emergency services
like fire station, healthcare etc.
Mobility model is divided into sub-models are
Environmental model
Movement pattern model
Signal Blocking model
17-Apr-2014V.Vasanthi-10JLDRCS002 4
6. To Create a new movement model
Movement patterns of all types of nodes that are
suitable for any environment without any predefined
paths in Ad-hoc Wireless sensor Network
Incorporate obstacles
Construct realistic movement (i.e) all types of Real
Environment
Determine movement pattern, signal blocking and
environment regions created by obstacles
17-Apr-2014V.Vasanthi-10JLDRCS002 6
7. 17-Apr-2014V.Vasanthi-10JLDRCS002 7
– The Existing Mobility Model mainly focused on movement
patterns of the nodes that are suited for limited
Environments with predefined paths.
– Few existing models does not consider obstacles. In
Obstacle Mobility model a Pre-defined pathways are used to
analyze the movement patterns.
– Mission Critical Mobility(MCM) model the nodes movement
in the simulation terrain without restrictions where the edge
detection is followed.
– To solve the above problems a new model was proposed by
using graph theory technique for movement patterns of all
types of nodes that are suitable for any environment without
any predefined paths.
8. Survey of Existing Models
Performance Analysis of existing Mobility Models
Design of a new Realistic obstacle based mobility
Model (OAGM)
• Performance analysis of Proposed model.
17-Apr-2014V.Vasanthi-10JLDRCS002 8
11. 17-Apr-2014V.Vasanthi-10JLDRCS002 11
Mobility Models Type Movement pattern
Random Walk Mobility Model
(Zonoozi & Dassanayake)
Entity model
Randomly chooses S/D with
TI
Random Waypoint Mobility Model
(Johnson)
Entity model
Select the destination
randomly and distributed
SPD
Random Direction Mobility Model
(Johnson)
Entity Model Change S/D in time Slot
Realistic mobility model
(A.Kamal, J.AI-Karaki)
Entity Model
S/D follows Distributed
Nodes
A Boundless Simulation Area Mobility
Model
(Z.Hass)
Entity Model Pre-S/D follows with new
12. 17-Apr-2014V.Vasanthi-10JLDRCS002 12
Mobility Models Type Movement pattern
Gauss-Markov Mobility Model
(Z.Hass)
Temporal
dependency model
Different level of parameters
City Section Mobility Model
(V.Davies )
Temporal
Dependency
Street in a city -Realistic movement
Reference Point Group Model
(X. Hong, M. Gerla, G. Pei, C. –
C. Chiang)
Group Model Group leader-Member
Column Mobility Model
(Sanchez)
Group Model Straight line- change in time slot
Pursue mobility model
(Sanchez)
Group Model Chance the target
Nomandic Community mobility
model (Sanchez):
Group Model Common reference point
Manhattan mobility model
(F.Bai , sadagopan, A.helmy)
Geographic/Realist
ic model
Vanet-Urban area-vertical/Horizontal
13. Mobility Models Type Movement pattern
Obstacle Mobility Model
( A.Jardosh)
Realistic/Geographic
Restriction model
pre-defined path(Voronoi
diagram-obstacle)
Pathway mobility model
( J.Tian )
Realistic/Geographic
Restriction model
Predefined edges –street and
pathways
Freeway mobility model
( F.Bai ,N.sadagopan, A.helmy )
Geographic Restriction
model
Lane of a freeway
Environment mobility model
( H.Babaei )
Geographic Restriction
model
Geometric and non-geometric
with different factors
Obstacle aware mobility model
( S.Ahmed ):
Geographic Restriction
model
Anchor concept, Not consider
Ad-hoc
Obstacle based on social networks
( P.Venkateswaran)
Geographic Restriction
model
Social network-with obstacle
Mission Critical mobility model
( C.Papageorgiou)
Geographic Restriction
model
Add-on of OM model-
Emergency, health care etc.
17-Apr-2014V.Vasanthi-10JLDRCS002 13
14. Proactive protocols
◦ Traditional distributed shortest-path protocols
◦ Maintain routes between every host pair at all times
◦ Based on periodic updates; High routing overhead
◦ Example: DSDV (destination sequenced distance vector)
Reactive protocols
◦ Determine route if and when needed
◦ Source initiates route discovery
◦ Example: DSR (dynamic source routing-Johnson96)
Hybrid protocols
◦ Adaptive; Combination of proactive and reactive
◦ Example : ZRP (zone routing protocol)
17-Apr-2014V.Vasanthi-10JLDRCS002 14
15. Reactive or On Demand
Developed at CMU in 1996
Route discovery cycle used for route finding – on Demand
Maintenance of active routes
No periodic activity of any kind – Hello Messages in AODV
Utilizes source routing (entire route is part of the header)
Use of caches to store routes
Supports unidirectional links -> Asymmetric routes are
supported
17-Apr-2014V.Vasanthi-10JLDRCS002 15
16. Routes maintained only between nodes who need to
communicate
reduces overhead of route maintenance
Route caching can further reduce route discovery
overhead
A single route discovery may yield many routes to the
destination, due to intermediate nodes replying from
local caches
17-Apr-2014V.Vasanthi-10JLDRCS002 16
17. Mobility
Models
Average
Connectivity
Graph
Protocol
Performance
Performance
Metrics
Random waypoint(RWP)
Reference Point Group Mobility(RPGM)
Gauss-Markov Mobility model(GM)
Manhattan Mobility Model(MHN)
Mission Critical Model(MCM)
1.Generated Packets
2.Packet Delivery Ratio%
3.End to End Delay
4.Dropped data
5.Control Overhead
6.Received Packets
DSR
17-Apr-2014V.Vasanthi-10JLDRCS002
17
Performance of Different Mobility models
18. 17-Apr-2014V.Vasanthi-10JLDRCS002 18
Duration 300s
Traffic Sources CBR, 512 byte packet, 4 packets per second
Transport protocol UDP
MAC protocol Mac/802.11
N/W interface Phy/wireless phy
Propagation model Two ray ground
Radius of node 250m
Antenna Omni Antenna
Area Size 1000m*1000m
Mobility Models RWP,MHN,RPGM,MCM,GM, OAGM
No of Nodes 50-250 (interval of 50)
Speed m/s 0-10m/s (interval of 2m/s)
Table: Simulation Parameter set
19. Performance metrics:
1.Generated Packets: The Number of packets send.
17-Apr-2014V.Vasanthi-10JLDRCS002 19
No of
Nodes
50 100 150 200 250
No of
Packets
generated
3480 5798 9272 11586 13898
Simulation Results
Here, all the mobility models use the nodes 50-250
(with the interval nodes of 50) with different Speed
0 to 10 ms with the time interval of 2ms (maximum
speed = 10 m/s). The Generated Packets (GP)
remains same even in the change of number of
Speed varies.
20. 17-Apr-2014V.Vasanthi-10JLDRCS002 20
The ratio of the data packets delivered to the destinations
to those generated by the sources. Mathematically, it can
be expressed as:
where p is the Ratio of successfully delivered packets, c
is the total number of flow or connections, f is the unique
flow id serving as index, Rf is the count of packets
received from flow f and Nf is the count of packets
transmitted to f.
21. This includes all possible delays caused by buffering during route
discovery latency, queuing at the interface queue, retransmission
delays at the MAC, and propagation and transfer times. It can be
defined as:
where D is the number of successfully received packets, i is
unique packet identifier, ri is time at which a packet with unique
id i is received, si is time at which a packet with unique id i is sent
and D is measured in ms. It should be less for high performance.
17-Apr-2014V.Vasanthi-10JLDRCS002 21
Performance metrics
22. 17-Apr-2014V.Vasanthi-10JLDRCS002 22
The ratio of the data packets not delivered to the
destinations to those generated by the sources.
Mathematically, it can be expressed as:
where DP is the Number of Dropped Packets, i is unique
packet identifier, ri is time at which a packet with unique
id i is received, si is time at which a packet with unique
id add with it and N is the number of connections, flows,
i is sent .
Performance metrics
24. 17-Apr-2014V.Vasanthi-10JLDRCS002 24
6.Received Packets (RP)
It is defined as number of packets received to the
destination successfully. It is declared as Rf i.e
count of packets received from flow f
Performance metrics
25. Random Models are not realistic.
Group models will take more time to reach
Destination from source.
Geographic Restriction Models use obstacle by
assumption in the simulation terrain which is not
realistic.
Obstacle models are restricted in pre-defined
pathways.
The MCM model nodes moves to the destination
through the edges of obstacles.
These models are not in real-life trace. The MCM
model is best suited only for emergency and health-
care. In these models PDR is low result and End to
End delay high variance.
17-Apr-2014V.Vasanthi-10JLDRCS002 25
26. Networks can be represented by graphs
The mobile nodes are vertices
The communication links are edges
17-Apr-2014V.Vasanthi-10JLDRCS002 26
Vertices
Edges
27. In this model the Features are as follows
1. Node Movement process
2. Hierarchical node organization
3. Physical obstacle placement
4. Source selection and Destination selection
17-Apr-2014V.Vasanthi-10JLDRCS002 27
28. 17-Apr-2014V.Vasanthi-10JLDRCS002 28
Step1 : Placement of Obstacle i.e. Rectangle or Square
Step2 : Placement of nodes randomly
Step3: Select the nodes initial point and obstacle position are
stored in files
Step4 : Movement process using graph theory(Hybrid bellman-
ford Dijkstra )
Step5 : Selecting min and max Speed
Step6 : Shortest path is finding and then repeat until it
reach the Destination
Step7: Check whether obstacle is available if not reach
the destination
Step8: If is obstacle is available then step 4
Step 9: Till Simulation time ends
Step 10: Stop process
29. 17-Apr-2014V.Vasanthi-10JLDRCS002 29
Algorithm1: The Movement Node Process
i = 0
Ci S
While there is an obstacle between Ci and D do
if ||D - Ni1|| ≤ ||D - Ni2|| then
if Mindis(V1,V2) = =1
Q1=Rp(V)
Else
Ni1V (Mindis[V1|V2])
Qi Ni1
else
QiNi2
end if
Q Q + {Qi}
Ci+1 Qi
i i + 1
end while
Qi D
RETURN Q
Features of the Proposed OAGM
1.Node Movement Process
30. 17-Apr-2014V.Vasanthi-10JLDRCS002 30
Figure 1: An example of how a node moves towards its destination point
around the obstacles in the network area according to the Proposed mobility
model.
31. Figures: An example of how a node moves towards its destination point around the
obstacles in the network area according to the Proposed mobility model.
17-Apr-2014V.Vasanthi-10JLDRCS002 31
32. 17-Apr-2014V.Vasanthi-10JLDRCS002 32
Hybrid Bellman ford algorithm to find the
shortest path
Initialization
d(v) ∞← , for all v є V
π(v) ← nil, for all v є V
d(s) ← 0
Relax(u, v)
if d(u) + c(u, v) < d(v)
d(v) ← d(u) + c(u, v)
π (v) ← u
Plain scan
for each edge (u, v) є E
Relax(u, v)
Dijkstra scan
S ← є
while (there is a vertex in V S with d < ∞) do
find vertex u in V S with the minimal value of d
S ← S {є u}
for each edge (u, v) ∈ E /* scanning u */
Relax(u, v)
Dijkstra(G, s)
Initialization
Dijkstra scan
return(d, )
Bellman-Ford(G, s)
33. 17-Apr-2014V.Vasanthi-10JLDRCS002 33
Initialization
i ← 0
do
i++
Plain scan
until ((there was no change of d at Plain scan) or (i = |V |))
if (i < |V |) return(d, )
else return(”There exists a negative cycle reachable from s.”)
Algorithm Bellman-Ford-Dijkstra (BFD) is as follows:
Bellman-Ford-Dijkstra (G, s)
Initialization
i ← 0
do
i++
Dijkstra scan
until ((there was no change of d at Dijkstra scan) or (i = |V | − 1))
if (i < |V | − 1) return(d, )
else return(”There exists a negative cycle reachable from s.”)
Notice : BFD may be considered as a particular version of BF, since at each
round, Relax is executed on all edges reachable from s.
34. 17-Apr-2014V.Vasanthi-10JLDRCS002 34
2.Hierarchical Node Organization
•The nodes are organized in groups with a pre-
defined leader/group.
•GS Each group contains certain no of nodes.
•GS is a parameter that can be act based on specific
characteristics of the scenarios.
•Each member group is set the Destination selection
and a point within a constant distance from its leader’s
destination point referred as distance and begins
towards it.
35. The obstacle can be placed anywhere inside the
simulation area.
The shape normally assumed is rectangle or square.
We select the four corners as the block edges and
store them in a file to be used during mobility
generation.
The obstacle has to be placed before we place the
nodes in their initial position.
17-Apr-2014V.Vasanthi-10JLDRCS002 35
36. The source and destination nodes are selected
randomly from the total no. of nodes simulated.
We have taken approximately 5% of nodes in
communication at any given time during the
simulation interval.
Total 10% of the nodes will be either source or
destination and remaining nodes will work as
forwarding nodes.
17-Apr-2014V.Vasanthi-10JLDRCS002 36
63. – Existing mobility models like obstacle mobility model
forces the nodes to move in a predefined pathways still
some pathways will result in congestion.
– The Mission Critical Model(MCM) is a realistic model
that are restricted to the environment like Health Care
and Emergency services.
– The proposed Obstruction Avoidance Generously
Mobility(OAGM) model is realistic too, and can able to
place obstacle any where in the simulation terrain in
user friendly manner and suitable for any environment.
– The Overall Performance Result of this model gives
higher percentage of 2% than MCM mobility model.
17-Apr-2014V.Vasanthi-10JLDRCS002 63
65. I thank Karpagam University and Karpagam trust
members for doing my research at this
university with award of KURF supported Grant
Reference No:2265
17-Apr-2014V.Vasanthi-10JLDRCS002 65
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