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Energy efficient node deployment for target coverage in wireless sensor network

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Gaurang Rathod
Gaurang Rathod

Network lifetime plays an integral role in setting up an efficient wireless sensor network. Coverage in a network needs to guarantee that the region is monitored with the required degree of reliability. Locations of sensor nodes constitute the basic input for the algorithms that examine coverage of the network. Coverage problems can be broadly classified as area coverage problem and target coverage problem. Area coverage focuses on monitoring the entire region of interest, whereas target coverage concerns monitoring only certain specific points in a given region. Target coverage can be categorized as simple coverage, k-coverage and Q-coverage. Lower coverage level (simple coverage) is enough for environmental or habitat monitoring or applications like home security. Higher degree of coverage (k-coverage) will be required for some applications like target tracking to track the targets accurately, or if sensors work in a hostile environment such as battle fields or chemically polluted areas. More reliable results are produced for higher degree of coverage which requires multiple sensor nodes to monitor the region/targets. An example of Q-coverage is video surveillance systems deployed for monitoring hostile territorial area where some sensitive targets like a nuclear plant may need more sensors cooperate to ensure source redundancy for precise data. Sensor nodes deterministically deployed by using artificial bee colony algorithm, so as to achieve the required target coverage level and maximize the network lifetime.

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Energy Efficient Node Deployment for Target Coverage in Wireless Sensor Network Prepared By : Gaurang Rathod ME EC Gujarat Technological University Gujarat India 29 January 2015 rathodgaurang@hotmail.com
Outline  Motivation  Introduction  Network Lifetime for Target Coverage  ABC Algorithm  Simulation Work  References
Motivation  Wireless sensor network is energy constrain network  Energy consumption of Node[1] 1. Data transmission 2. Signal processing 3. Hardware operation
Target Coverage  Coverage can be classified as area coverage and target(point) coverage[6] (a) Area coverage and (b) Point coverage
Continue…  Target Coverage can be categorized 1. Simple coverage 2. k-coverage 3. Q-coverage: Target T= {T1,T2,..,Tn} should be monitored by Q= {q1, q2,…, qn} number of nodes
Network Lifetime  Network is live till all targets being sensed by nodes otherwise network considered as dead.  Network life is defined by target sensed time duration by nodes.  Deploy nodes such a way that target sensed by maximum number of nodes, so network live long.
Node Deployment Algorithm  Input: no. of nodes and no. of target  Output: optimum location of node such that maximum network life achieve with required target coverage level  Procedure 1. Select random location for the given no. of target. 2. Deploy nodes randomly such that each target must be covered by minimum one node. 3. Compute life time of network. 4. Recomputed node position using ABC algorithm such that network life maximum.
Network Lifetime Calculation  Let sensor nodes : {s1, s2, s3,…,sm} randomly deployed to cover the region R with n targets : {T1,T2,..,Tn}  Each node has initial energy E0 and a sensing radius sr  A sensor node is said to cover target if distance between node and target is less than radius sr  Coverage Matrix is defined as 1 0 i j i j if S monitorsT M otherwise    
Continue…  where ei is energy consumption rate of i-node   For k-coverage, qj=k, j=1,2,…,n 0 ( ) , 1Lifeof node i i E b i m e    1 * minNetworklifetime m i j i i j M b j q         
Artificial Bee Colony Algorithm[10]  The colony of artificial bees contain three group of bees 1. Employed bees 2. Onlookers 3. Scouts  Employed bees determine a food source within the neighborhood of the food source in their memory  Employed bees share information with onlookers within the hive and then the onlookers select one of the food sources
Continue…  Onlookers select a food source within the neighborhood of the food sources chosen by themselves  An employed bee of which the source has been abandoned becomes a scout and starts to search a new food source randomly
ABC Algorithm Flow Chart
Continue.. Image source: http://commons.wikimedia.org/wiki/File:Maxima_and_Minima.svg
Continue…  New search position i=bee index j=random selected dimension i.e. either x-yam or y-yam random selected k=random selected bee (k never equal to i) , , , ,( )i j i j i j k jv x x x   
Simulation A. Target Coverage B. Importance of Deployment and Scheduling
Experiment Work A 1. For fix number of targets and varying number of nodes 2. For different-different number of targets and nodes 3. For changing size of network 4. By varying sensing range of node
Simulation Parameters Parameter Value Network area 400m x 400m 500m x 500m Node sensing range 75m 80m Initial energy 100 J Energy consumption rate 1 J/S No. of target 20 to 40 No. of nodes 100 to 250
Network Lifetime for 20 Targets Network size: 500m x 500m, sensing range: 75m
Network Lifetime for 25 Targets Network size: 500m x 500m, sensing range: 75m
Network Lifetime for Network Size of 400mx400m Target: 25, sensing range: 75m
Network Lifetime for Network Size of 500mx500m Target: 25, sensing range: 75m
Network Lifetime for 75m Sensing Range of Node Target: 25, Network size: 500m x 500m
Network Lifetime for 80m Sensing Range of Node Target: 25, Network size: 500m x 500m
Network Lifetime for Random Deployment Network size: 500m x 500m, sensing range: 75m
Deployment using ABC algorithm Network size: 500m x 500m, sensing range: 75m
Network Lifetime for K-Coverage (Random Deployment) Network size: 500m x 500m, sensing range: 75m
Network Lifetime for K-Coverage (Deployment using ABC Algorithm) Network size: 500m x 500m, sensing range: 75m
Experiment Work B Simulation Cases : 1. Node deployment with same communication interval 2. Node deployment with distinct random communication interval 3. Node deployment with distinct communication interval base on communication cost
Simulation Parameters Parameter Value Channel Type Wireless 802.15 Propagation Type Two Ray Ground MAC protocol MAC – 802.15 Queue Type Drop tail Antenna Omni Antenna Number of nodes 25 Queue Length 50 Routing protocol AODV Network area 500 m x 500 m Packet size 200 bytes Initial Energy 2 joules 29
Case 1 : Node Deployment with Same Communication Interval
Residual Energy of All nodes vs Time
Case 2: Node Deployment with Distinct Random Communication Interval  Energy is inversely proportional to square of the distance  Node far from the base station consume more energy compared to near one  By allocating different communication interval to each node helpful to make network energy consumption rate balance compared to case 1
Residual Energy of All nodes vs Time
Case 3: Node Deployment with Distinct Communication Interval Based on Communication Cost
Case 1 : Energy Left at Simulation End Node Energy Node Energy Node Energy 0 1.5216 8 1.5206 16 1.3288 1 1.5066 9 1.5210 17 1.3287 2 1.5212 10 1.5211 18 1.5213 3 1.4236 11 1.4175 19 1.5219 4 1.5216 12 1.4927 20 1.5080 5 1.5077 13 1.5207 21 1.5215 6 1.5215 14 1.4755 22 1.5204 7 1.5218 15 1.5209 23 1.6813 Difference between highest and lowest energy =0.3526 joule
Case 2 : Energy Left at Simulation End Node Energy Node Energy Node Energy 0 1.6736 8 1.6847 16 1.6849 1 1.6383 9 1.6450 17 1.6714 2 1.6823 10 1.6851 18 1.7172 3 1.6827 11 1.6812 19 1.6004 4 1.6968 12 1.6838 20 1.6808 5 1.6346 13 1.6857 21 1.6819 6 1.6686 14 1.6608 22 1.6600 7 1.6786 15 1.6441 23 1.6813 Difference between highest and lowest energy =0.1168 joule
Case 3 : Energy Left at Simulation End Node Energy Node Energy Node Energy 0 1.8562 8 1.8585 16 1.8534 1 1.8582 9 1.8563 17 1.8561 2 1.8592 10 1.8588 18 1.8572 3 1.8586 11 1.8576 19 1.8586 4 1.8566 12 1.8592 20 1.8527 5 1.8455 13 1.8589 21 1.8580 6 1.8592 14 1.8424 22 1.8554 7 1.8480 15 1.8597 23 1.8505 Difference between highest and lowest energy =0.0173 joule
Conclusion  Sensing range of node, size of network, number of target, number of nodes and scheduling have significant effect on life of network which we have done analyses in the simulation by increasing no. of target and sensing area network life decrease but by increasing node’s sensing radius life increases with effective coverage level.  By using artificial bee colony algorithm for node deployment, we achieve the required target coverage level and maximize the network lifetime compared to random deployment. Node deployment by using ABC algorithm work good for simple as well as k-coverage application.
References 1. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks”, IEEE Commun. Mag., vol. 40, no. 8, pp. 102– 114, Aug. 2002. 2. Karl, Holger and Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, John Wiley & Son Ltd, 2005. 3. I. Akyildiz and M. Vuran, “Wireless Sensor Networks”, John Wiley & Son Ltd, 2010. 4. Datasheet of Mica2 mote. 5. G. Anastasi, M. Conti, M. Francesco and A. Passarella, “Energy conservation in wireless sensor networks A survey”, Elsevier Ad Hoc Networks, pp. 537-568, July 2008. 6. S. Mini, S. Udgata, and S. Sabat, “Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks”, IEEE sensor journal, vol. 14, no. 3, March 2014.
Continue… 7. M. Cardei and J. Wu, “Energy-efficient coverage problems in wireless ad-hoc sensor networks”, Elsevier- Computer Communications, December 2004. 8. X. Tang and J. Xu, “Optimizing lifetime for continuous data aggregation with precision guarantees in wireless sensor networks”, IEEE/ACM transactions on networking, vol. 16, no. 4, August 2008. 9. C. Wang, J. Shih, B. Pan and T. Wu, “A network lifetime enhancement Method for sink relocation and its analysis in wireless Sensor Networks”, IEEE sensors journal, vol. 14, no. 6, june 2014. 10. D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm”, Science direct-Applied Soft Computing 8, pp. 687-697, 2008. 11. Network Simulator -2 http://www.isi.edu/nsnam/ns/doc/index.html
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Energy efficient node deployment for target coverage in wireless sensor network

  • 1. Energy Efficient Node Deployment for Target Coverage in Wireless Sensor Network Prepared By : Gaurang Rathod ME EC Gujarat Technological University Gujarat India 29 January 2015 rathodgaurang@hotmail.com
  • 2. Outline  Motivation  Introduction  Network Lifetime for Target Coverage  ABC Algorithm  Simulation Work  References
  • 3. Motivation  Wireless sensor network is energy constrain network  Energy consumption of Node[1] 1. Data transmission 2. Signal processing 3. Hardware operation
  • 4. Target Coverage  Coverage can be classified as area coverage and target(point) coverage[6] (a) Area coverage and (b) Point coverage
  • 5. Continue…  Target Coverage can be categorized 1. Simple coverage 2. k-coverage 3. Q-coverage: Target T= {T1,T2,..,Tn} should be monitored by Q= {q1, q2,…, qn} number of nodes
  • 6. Network Lifetime  Network is live till all targets being sensed by nodes otherwise network considered as dead.  Network life is defined by target sensed time duration by nodes.  Deploy nodes such a way that target sensed by maximum number of nodes, so network live long.
  • 7. Node Deployment Algorithm  Input: no. of nodes and no. of target  Output: optimum location of node such that maximum network life achieve with required target coverage level  Procedure 1. Select random location for the given no. of target. 2. Deploy nodes randomly such that each target must be covered by minimum one node. 3. Compute life time of network. 4. Recomputed node position using ABC algorithm such that network life maximum.
  • 8. Network Lifetime Calculation  Let sensor nodes : {s1, s2, s3,…,sm} randomly deployed to cover the region R with n targets : {T1,T2,..,Tn}  Each node has initial energy E0 and a sensing radius sr  A sensor node is said to cover target if distance between node and target is less than radius sr  Coverage Matrix is defined as 1 0 i j i j if S monitorsT M otherwise    
  • 9. Continue…  where ei is energy consumption rate of i-node   For k-coverage, qj=k, j=1,2,…,n 0 ( ) , 1Lifeof node i i E b i m e    1 * minNetworklifetime m i j i i j M b j q         
  • 10. Artificial Bee Colony Algorithm[10]  The colony of artificial bees contain three group of bees 1. Employed bees 2. Onlookers 3. Scouts  Employed bees determine a food source within the neighborhood of the food source in their memory  Employed bees share information with onlookers within the hive and then the onlookers select one of the food sources
  • 11. Continue…  Onlookers select a food source within the neighborhood of the food sources chosen by themselves  An employed bee of which the source has been abandoned becomes a scout and starts to search a new food source randomly
  • 14. Continue…  New search position i=bee index j=random selected dimension i.e. either x-yam or y-yam random selected k=random selected bee (k never equal to i) , , , ,( )i j i j i j k jv x x x   
  • 15. Simulation A. Target Coverage B. Importance of Deployment and Scheduling
  • 16. Experiment Work A 1. For fix number of targets and varying number of nodes 2. For different-different number of targets and nodes 3. For changing size of network 4. By varying sensing range of node
  • 17. Simulation Parameters Parameter Value Network area 400m x 400m 500m x 500m Node sensing range 75m 80m Initial energy 100 J Energy consumption rate 1 J/S No. of target 20 to 40 No. of nodes 100 to 250
  • 18. Network Lifetime for 20 Targets Network size: 500m x 500m, sensing range: 75m
  • 19. Network Lifetime for 25 Targets Network size: 500m x 500m, sensing range: 75m
  • 20. Network Lifetime for Network Size of 400mx400m Target: 25, sensing range: 75m
  • 21. Network Lifetime for Network Size of 500mx500m Target: 25, sensing range: 75m
  • 22. Network Lifetime for 75m Sensing Range of Node Target: 25, Network size: 500m x 500m
  • 23. Network Lifetime for 80m Sensing Range of Node Target: 25, Network size: 500m x 500m
  • 24. Network Lifetime for Random Deployment Network size: 500m x 500m, sensing range: 75m
  • 25. Deployment using ABC algorithm Network size: 500m x 500m, sensing range: 75m
  • 26. Network Lifetime for K-Coverage (Random Deployment) Network size: 500m x 500m, sensing range: 75m
  • 27. Network Lifetime for K-Coverage (Deployment using ABC Algorithm) Network size: 500m x 500m, sensing range: 75m
  • 28. Experiment Work B Simulation Cases : 1. Node deployment with same communication interval 2. Node deployment with distinct random communication interval 3. Node deployment with distinct communication interval base on communication cost
  • 29. Simulation Parameters Parameter Value Channel Type Wireless 802.15 Propagation Type Two Ray Ground MAC protocol MAC – 802.15 Queue Type Drop tail Antenna Omni Antenna Number of nodes 25 Queue Length 50 Routing protocol AODV Network area 500 m x 500 m Packet size 200 bytes Initial Energy 2 joules 29
  • 30. Case 1 : Node Deployment with Same Communication Interval
  • 31. Residual Energy of All nodes vs Time
  • 32. Case 2: Node Deployment with Distinct Random Communication Interval  Energy is inversely proportional to square of the distance  Node far from the base station consume more energy compared to near one  By allocating different communication interval to each node helpful to make network energy consumption rate balance compared to case 1
  • 33. Residual Energy of All nodes vs Time
  • 34. Case 3: Node Deployment with Distinct Communication Interval Based on Communication Cost
  • 35. Case 1 : Energy Left at Simulation End Node Energy Node Energy Node Energy 0 1.5216 8 1.5206 16 1.3288 1 1.5066 9 1.5210 17 1.3287 2 1.5212 10 1.5211 18 1.5213 3 1.4236 11 1.4175 19 1.5219 4 1.5216 12 1.4927 20 1.5080 5 1.5077 13 1.5207 21 1.5215 6 1.5215 14 1.4755 22 1.5204 7 1.5218 15 1.5209 23 1.6813 Difference between highest and lowest energy =0.3526 joule
  • 36. Case 2 : Energy Left at Simulation End Node Energy Node Energy Node Energy 0 1.6736 8 1.6847 16 1.6849 1 1.6383 9 1.6450 17 1.6714 2 1.6823 10 1.6851 18 1.7172 3 1.6827 11 1.6812 19 1.6004 4 1.6968 12 1.6838 20 1.6808 5 1.6346 13 1.6857 21 1.6819 6 1.6686 14 1.6608 22 1.6600 7 1.6786 15 1.6441 23 1.6813 Difference between highest and lowest energy =0.1168 joule
  • 37. Case 3 : Energy Left at Simulation End Node Energy Node Energy Node Energy 0 1.8562 8 1.8585 16 1.8534 1 1.8582 9 1.8563 17 1.8561 2 1.8592 10 1.8588 18 1.8572 3 1.8586 11 1.8576 19 1.8586 4 1.8566 12 1.8592 20 1.8527 5 1.8455 13 1.8589 21 1.8580 6 1.8592 14 1.8424 22 1.8554 7 1.8480 15 1.8597 23 1.8505 Difference between highest and lowest energy =0.0173 joule
  • 38. Conclusion  Sensing range of node, size of network, number of target, number of nodes and scheduling have significant effect on life of network which we have done analyses in the simulation by increasing no. of target and sensing area network life decrease but by increasing node’s sensing radius life increases with effective coverage level.  By using artificial bee colony algorithm for node deployment, we achieve the required target coverage level and maximize the network lifetime compared to random deployment. Node deployment by using ABC algorithm work good for simple as well as k-coverage application.
  • 39. References 1. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks”, IEEE Commun. Mag., vol. 40, no. 8, pp. 102– 114, Aug. 2002. 2. Karl, Holger and Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, John Wiley & Son Ltd, 2005. 3. I. Akyildiz and M. Vuran, “Wireless Sensor Networks”, John Wiley & Son Ltd, 2010. 4. Datasheet of Mica2 mote. 5. G. Anastasi, M. Conti, M. Francesco and A. Passarella, “Energy conservation in wireless sensor networks A survey”, Elsevier Ad Hoc Networks, pp. 537-568, July 2008. 6. S. Mini, S. Udgata, and S. Sabat, “Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks”, IEEE sensor journal, vol. 14, no. 3, March 2014.
  • 40. Continue… 7. M. Cardei and J. Wu, “Energy-efficient coverage problems in wireless ad-hoc sensor networks”, Elsevier- Computer Communications, December 2004. 8. X. Tang and J. Xu, “Optimizing lifetime for continuous data aggregation with precision guarantees in wireless sensor networks”, IEEE/ACM transactions on networking, vol. 16, no. 4, August 2008. 9. C. Wang, J. Shih, B. Pan and T. Wu, “A network lifetime enhancement Method for sink relocation and its analysis in wireless Sensor Networks”, IEEE sensors journal, vol. 14, no. 6, june 2014. 10. D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm”, Science direct-Applied Soft Computing 8, pp. 687-697, 2008. 11. Network Simulator -2 http://www.isi.edu/nsnam/ns/doc/index.html

Editor's Notes

  1. Benchmark function of the abc algo