Energy Efficient Data Gathering Protocol in WSN

4,373 views

Published on

Presentation outline:
Introduction
WSN basics
Protocols
EAR, 2002
CHIRON, 2009
ETR, 2009
REAR, 2011
Proposition of a novel Energy Efficient DGP
Conclusion

Published in: Education, Technology, Business
1 Comment
4 Likes
Statistics
Notes
No Downloads
Views
Total views
4,373
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
439
Comments
1
Likes
4
Embeds 0
No embeds

No notes for slide

Energy Efficient Data Gathering Protocol in WSN

  1. 1. ZUBIN BHUYANCSI 11014STCN Seminar
  2. 2. Outline Introduction WSN basics Protocols EAR, 2002 CHIRON, 2009 ETR, 2009 REAR, 2011 Proposition of a novel Energy Efficient DGP Conclusion Reference2
  3. 3. Introduction WSN nodes have the ability to sense and process data wirelessly communicate with other nodes and a sinknode have the ability to collect data from other nodes gateway or a base station[1] (Liu, et al, IEEE ICC 2007 proc.)3ENVIRONMENTEVENTS
  4. 4. IntroductionChallenges & Constraints: Power Consumption Aggressive energy-scavenging policy required Low Cost Computation constraints Communication: Low Data Rates <<10Kbps Self-organization and Localization Redundancy in deployment Fault Tolerance Scalability…. and many more!!4
  5. 5. R.C. Shah, J.M Rabaey, “Energy Aware Routing for LowEnergy Ad Hoc Sensor Networks”, IEEE WCNC’02, pp. 350-355, March 2002EAR: Energy Aware Routing Protocol
  6. 6.  Destination initiated routing Directional flooding to determine variousroutes (based on location) Collect energy metrics along the way Every route has a probability of being chosen Probability 1/energy cost The choice of path is made locally at everynode for every packetEnergy Aware Routing6
  7. 7. Energy Aware Routing:Functioning Each node is addressable through class-basedaddressing, includes Location Type of the node Three phases of the protocol1. Setup phase or interest propagationo Localized flooding to find all the routes from source todestination and their energy costs2. Data Communication phase or data propagationo paths are chosen probabilistically for data transmission3. Route maintenanceo Localized flooding to keep paths alive and update routecost information7
  8. 8. Setup Phase:ControllerSensorDirectional flooding10 nJ30 nJ(0.75*10)+ (0.25*30)= 15 nJp1 = 0.75p2 = 0.25Local RuleEnergy Aware Routing † :Functioning8† Slide borrowed from Rahul C. Shah, Jan Rabaey, Berkeley Wireless Research Center,Dept. of EECS University of California, Berkeleyhttp://bwrc.eecs.berkeley.edu/publications/2002/presentations/WCNC2002/wcnc.ppt
  9. 9.  The metric can also include: Information about the data buffered for a neighbor Regeneration rate of energy at a node Correlation of datainitialremainingrxtxEEEEC )(Energy Aware Routing:Energy Cost9
  10. 10. 1.01.00.40.6ControllerSensor0.30.7Each node makes a localdecisionData Communication Phase:Energy Aware Routing:Functioning10
  11. 11. Energy Aware Routing:Simulation Results Energy Usage ComparisonDiffusion Routing Energy Aware RoutingPeak energy usage was ~50 mJ for 1 hour simulation11
  12. 12. Energy Aware Routing:Advantage Spread traffic over different paths; keep pathsalive without redundancy Mitigates the problem of hot-spots in thenetwork Has built in tolerance to nodes moving out ofrange or dying Continuously check different paths Simulation result shows improvement of 21.5% energy saving 44% increase in network lifetime over DirectedDiffusion12
  13. 13. Kuong-Ho Chen, Jyh-Ming Huang, Chieh-Chuan Hsiao,“CHIRON: An energy-efficient chain-based hierarchicalrouting protocol in wireless sensor networks”, IEEEWireless Telecommunications Symposium, 2009CHIRON: An Energy-Efficient Chain-BasedHierarchical Routing Protocol in WSN
  14. 14. CHIRON Energy efficient hierarchical chain-based routingprotocol Main idea: Split the sensing field into a smaller areas Create multiple shorter chains to reduce thedata transmission delay and redundant path Therefore effectively conserve the node energyand prolong the network lifetime14
  15. 15. CHIRON:Phases of operation Operation of CHIRON protocol consists of fourphases:1. Group Construction Phase.2. Chain Formation Phase.3. Leader Node Election Phase.4. Data Collection and Transmission Phase.15
  16. 16. CHIRON:Phases I1. Group Construction Phase: Divide the sensing field into anumber of smaller areas R: the transmission range of theBS. (1 … n) θ: the beam width of the directionalantenna of BS (1….m) Gθ, R: Group id. By changing R andθ, n*m groups can be defined After the sensor nodes arescattered, the BS graduallysweeps the whole sensing area bychanging Tx power level, R, θ.16
  17. 17. CHIRON:Phases II2. Chain Formation Phase: The nodes within each group Gx,y will be linkedtogether to form a chain Cx,y Chain formation process is same as that in PEGASISscheme the node farthest away from the BS is initiated tocreate the group chain Greedily add nearest node of last chained nodeto the chain Repeat until all nodes are put together17
  18. 18. CHIRON:Phases III3. Leader Node Election Phase: Node with maximum residualenergy becomes leader For first round, the nodefarthest away from the BS isassigned to be the groupchain leader Thereafter, for each datatransmission round, the nodewith the maximum residualenergy is elected. Residual power information ofnodes can be piggybackedwith fused data18
  19. 19. CHIRON:Phases IV4. Data collection &Transmission Phase: Nodes transmit along thechain to chain leader Then, starting from thefarthest group multi-hopleader-by-leader aggregatedtransmission is made to BS Neighbouring leader iselected as relaying node if it isnearer to BS than any otherCL19
  20. 20. CHIRON:Performance comparisons20
  21. 21. CHIRON:Performance comparisons21
  22. 22. Soyoung Hwang, Gwang-Ja Jin, Changsub Shin, BongsooKim, “Energy-Aware Data Gathering in Wireless SensorNetworks”, 6th IEEE Consumer Communications andNetworking Conference, 2009ETR: Energy Aware Tree Routing Protocol
  23. 23. ETR: Energy Aware Tree Routing Protocol Tree structure used to route data Multi-hop route Three phases: Route setup Data Delivery Path maintenance23
  24. 24. ETR:Phase I Route Setup: In the first phase, a hierarchicaltopology is created Sink node is assigned Level 0 It broadcasts route setup message with its addressand level On receiving route setup message a node sets itslevel to {parent_level+1} and the sender as parent The steps are repeated until all nodes are included24
  25. 25. ETR:Phase I25Route Setup: Nodeselects another nodeas its parent node ifit has lowest levelfrom received routesetup messages.
  26. 26. ETR:Phase II Data delivery: Data is routed to the sink node. sensor node transmits a data message includingits own address, a destination address set to itsparent On receiving parent transmits acknowledgement If a parent fails, node selects neighbour withhighest residual energy as parent26
  27. 27. ETR:Phase III Path maintenance: Considers residual energy of nodes Data messages have Residual Energyinformation of the node Any data transmitted is received by allneighbouring nodes A candidate is selected as parent based on thislist of neigbours27
  28. 28. ETR:Performance28Average residual energy Network lifeime
  29. 29. Jin Wang, Tinghuai Ma, Jinsung Cho, and Sungoung Lee,“An Energy Efficient and Load Balancing RoutingAlgorithm for Wireless Sensor Networks”, ComSIS Vol. 8,No. 4, Special Issue, October 2011REAR: Ring-based Energy AwareRouting
  30. 30. REAR Motivation: Hotspot issue still an open problem Nodes on the shortest path or close to the BS depleteenergy quickly REAR aims to achieve both energy balancing andenergy efficiency for all nodes Multi-hop route is built by BS in a centralized way: BS has more powerful resources such as memory,computation and communication Algorithm considers: Primary metric: Hop number and distance Secondary metric: Residual energy30
  31. 31. REAR:Algorithm1. If the source to BS distance d < ∑d(ni), use direct transmission2. else, broadcast a multi-hop request to BS3. BS determines the final multi-hop route with the optimal number nand distances {d1, …., dn}4. BS builds ring structure with different ring size5. Classify nodes into different levels based on ring size6. BS will determine the final multi-hop route as follows: Choose some nodes from level n such that di,j ∈ (dn, dn + Δ) Within these, BS will choose those which belong to level (n+1) tomake progress from source to BS BS will choose the one from level (n+1) with maximal remainingenergy as the final next hop node Source node will start the transmission of its data when itreceives the complete multi-hop route information31
  32. 32. REAR:WSN structureBS oriented ring-structure32
  33. 33. REAR:Experimental Results Average hop numberdecreases as thetransmission radius Rincreases When 140≤R ≤220REAR outperformsgreedy algorithm33
  34. 34. REAR:Experimental Results R = 110m Area = 20 m2 Averaging doneover 100 differentnetwork topologysimulation result REAR algorithm hasthe longest lifetime34
  35. 35. A Proposal: Novel WSN routing protocol basedon energy dissipation history
  36. 36. Network Survivability †Critical node to maintain networkconnectivityCritical node as it isthe only one of its type•Delay the death of highly active nodes ensuring long network lifetime•Load balancing•Predict nodes that may die early† Images from Rahul C. Shah, Jan Rabaey, Berkeley Wireless Research Center, Dept. of EECSUniversity of California, Berkeleyhttp://bwrc.eecs.berkeley.edu/publications/2002/presentations/WCNC2002/wcnc.ppt36
  37. 37. Routing based on Energy UsageHistory in WSN Highly active nodes should not be used for common orperiodic/routine chain transmissions Aim to reroute data transmission paths along nodes thatare less active Energy Usage Index(EUI)calculated before everytransmission Use „energy spent per second’ for last λ seconds EUI, Residual Energy Level piggybacked on datapackets. Neighbouring nodes can overhear transmissions andwill know about other nodes‟ EUI Prevention is better than cure: Identify highly active nodes beforehand37
  38. 38. Routing based on Energy UsageHistory in WSN Past-information about energy dissipation of nodes mayimprove network lifetime EWMA: applies weighting factors which decreaseexponentiallyEUIt = α x Et + (1 - α) x EUIt-1 Weighting for each older data point decreasesexponentially, giving much more importance to recentobservations while still not discarding older observationsentirely.38EWMA weights,N = 15
  39. 39. Routing based on Energy UsageHistory in WSN Energy Usage Index (EUI): Indicates at what rate anode is using up its energy Distance from BS (DB): parameter that restricts thedelay in propagation Residual Energy (RE): Current energy level These three parameters are used to select next-hopnode for the route Nodes know only about their next-hop neighbours info Node Ni forwards to neighbour NJ if ∀ neighbour ofcurrent node Ni, NJ hasmin(Total Cost Index = α x EUI + β x DB + γ x RE) α, β, γ parameters can be adjusted as required.39
  40. 40. High energy dissipationzones: Areas of highactivityDipRouting based on Energy UsageHistory in WSN Highly active nodes are not over-burdenedwith extra transmission load by its neighborsGraphical representation of spatialenergy dissipation in a random WSNnode dispersionBS40
  41. 41. Routing based on Energy Usage History in WSN:Possible directions of further investigation How to use it in a clustered-based approach? Can EUI be calculated for a sub-region,partition, cluster? Can α, β, γ parameters be automaticallyadapted (by cluster heads, neighbours)? Simulation and comparison with otherprotocols.41
  42. 42. CONCLUSION Network performance is application dependent Need to clearly identify metrics of interest Trade-off: Accuracy vs. Latency vs. Lifetime vs. ….. Research directions Routing graphs: selecting a tree, transmissionschedule, maintenance policy Power aware routing: enhanced link sharing, loadbalancing, improving lifetitme Optimality in Algorithms Open Problems everywhere!!42
  43. 43. References[1] Ming Liu, Yuan Zheng, Jiannong Cao, Guihai Chen, Lijun Chen,Haigang Gong, “AnEnergy-Aware Protocol for Data Gathering Applications in Wireless SensorNetworks”, IEEE Communications Society subject matter experts for publication inthe ICC 2007 proceedings[2] R.C. Shah, J.M Rabaey, “Energy Aware Routing for Low Energy Ad Hoc SensorNetworks”, IEEE WCNC’02, pp. 350-355, March 2002[3] Kuong-Ho Chen, Jyh-Ming Huang, Chieh-Chuan Hsiao, “CHIRON: An energy-efficient chain-based hierarchical routing protocol in wireless sensornetworks”, IEEE Wireless Telecommunications Symposium, 2009[4] Jin Wang, Tinghuai Ma, Jinsung Cho, and Sungoung Lee, “An Energy Efficient andLoad Balancing Routing Algorithm for Wireless Sensor Networks”, ComSIS Vol.8, No. 4, Special Issue, October 2011[5] K.Ramanan, E.Baburaj, “Data Gathering Algorithms For Wireless SensorNetworks: A Survey”, International Journal of Ad hoc, Sensor & UbiquitousComputing (IJASUC) Vol.1, No.4, December 2010[6] S. Jamal N. Al-karaki, Ahmed E. Kamal, ”Routing Techniques In Wireless SensorNetworks: A Survey”, IEEE Wireless Communications • December 200443
  44. 44. References[8] S. M. Jung, Y. J. Han, and T. M. Chung, “The Concentric Clustering Scheme forEfficient Energy Consumption in the PEGASIS,” Proceedings of the 9thInternational Conference on Advanced Communication Technology, Vol. 1, pp. 260-265, 2007[9] Soyoung Hwang, Gwang-Ja Jin, Changsub Shin, Bongsoo Kim, “Energy-AwareData Gathering in Wireless Sensor Networks”, 6th IEEE ConsumerCommunications and Networking Conference, 2009Few images and slides have been take from the links given below:[10] http://www.cs.ucf.edu/~turgut/COURSES/EEL6788_ACN_Fall05/Lecture7-Oct05-05.ppt[11] http://bwrc.eecs.berkeley.edu/publications/2002/presentations/WCNC2002/wcnc.ppt[12] http://www.cs.binghamton.edu/~kang/teaching/cs580s/routing-survey.ppt[13] http://www.senmetrics.org/papers/Senmetrics-keyNote-Helmy-2.ppt44
  45. 45. Introduction: TaxonomyWSN protocols are classified according to their data delivery modelinto the following categories [Kulik, et al, 2002]:1. Continuous LEACH: For routing data to base stations in static WSN TEEN and PEGASIS: Improvements over LEACH2. Observer-initiated Directed Diffusion: Data/information are named using attribute-value pairs Interest based queries3. Event-driven SPIN: Set of negotiation based protocols4. Hybrid46
  46. 46. 47Energy conservation policies[2] Jones, Sivalingam, Agrawal, and Chen survey article in ACM WINET, July 2001[3] Lindsey, Sivalingam, and Raghavendra book chapter in Wiley Handbook of Mobile Computing, IvanStojmenovic, Editor, 2002Physical Layer •Low power circuit (CMOS, etc.) design•Optimum hardware, software function division•Energy effective waveform/ code design•Adaptive RF power controlMAC sub-layer • Energy effective MAC protocol• Collision free, reduce retransmission and transceiveron-times• Intermittent, synchronized operation• Rendezvous protocolsLink Layer • FEC versus ARQ schemes; Link packet length adapt.Network Layer • Multi-hop route determination• Energy aware route algorithm• Route cache, directed diffusionApplication Layer • Video applications: compression and frame-dropping• In-network data aggregation and fusion
  47. 47. C. Intanagonwiwat, R. Govindan and D. Estrin, “DirectedDiffusion: A scalable and robust communication paradigmfor sensor networks”, IEEE/ACM Mobicom, 2000Directed Diffusion protocol
  48. 48. Directed Diffusion Query-driven data delivery model Diffusing data by using a naming scheme named using attribute-value pairs Interest, data propagation and dataaggregation are determined by localinteractions Sink requests data by broadcasting interests Interest diffuses through the WSN hop-by-hopaccording to contents of the interest49
  49. 49. Directed Diffusion:Interest & Gradient Interest is generally given by the sink node For each active task, sink periodically broadcasts an interestmessage to each of its neighbors Sink periodically refreshes each interest by re-sending thesame interest with monotonically increasing timestampattribute for reliability purposes Every node maintains an interest cache where each item inthe cache corresponds to a distinct interest Interest entries in the cache do not contain information aboutthe sink Definition of distinct interests may allow interest aggregation The interest entry contains several gradient fields, up to oneper neighbor50
  50. 50. Directed Diffusion:Functioning Setting up Gradient: When a node receives an interest, itdetermines if the interest exists in the cache:1. If no matching exist, the node creates an interest entry This entry has single gradient towards the neighbor fromwhich the interest was received with specified data rate Individual neighbors can be distinguished by locally uniqueidentifiers2. If the interest entry exists, but no gradient for the sender ofinterest Node adds a gradient with the specified value Updates the entry‟s timestamp and duration fields3. If there exists both entry and a gradient, The node updates the entry‟s timestamp and duration fields51
  51. 51. Directed Diffusion:FunctioningData propagation Data message is unicast individually to the relevant neighbors A node receiving a data message from its neighbors checks to see if matchinginterest entry in its cache exists according the matching rules described1. If no match exist, the data message is dropped2. If match exists, the node checks its data cache associated with thematching interest entry If a received data message has a matching data cache entry, thedata message is dropped Otherwise, the received message is added to the data cache andthe data message is re-sent to the neighbors Data cache keeps track of the recently seen data items, preventing loops By checking the data cache, a node can determine the data rate of thereceived events52
  52. 52. Directed Diffusion:FunctioningDestinationSourceSetting up gradientsDestinationSourceSending dataoEvery node maintains an interest cacheoData message is unicast individually to the relevant neighbouroRecent data is cached to prevent loopingoReinforcement of one neighbor to draw higher qualityachieved by data driven local rules: observed losses, delay variancesoNegative reinforcement of certain paths: low resource levels, etc53
  53. 53. A. Manjeshwar , D. P. Agarwal, “TEEN: a Routing Protocol forEnhanced Efficiency in Wireless Sensor Networks,” 1st Int’l.Wksp. on Parallel and Distrib. Comp. Issues in WirelessNetworksand Mobile Comp., 2001Threshold sensitive Energy EfficientNetwork protocol
  54. 54. Threshold sensitive Energy EfficientNetwork protocol (TEEN) Hierarchical, cluster-based data-centricprotocol Designed to respond to sudden changes For time-critical applications Reactive network Nodes sense continuously, but datatransmission is done infrequently Control over energy consumption andaccuracy55
  55. 55. TEEN : Multi-level hierarchicalclustering56Clusters1st Level Cluster HeadSimple Node2nd Level Cluster HeadBase Station
  56. 56. TEEN: Functioning Every node in a cluster takes turns to become the CHfor a time interval called cluster period At every cluster change time the cluster-headbroadcasts to its members Hard threshold (HT) : A member only sends data to CH only ifdata values are in the range of interest Soft threshold (ST) : A member only sends data if its valuechanges by at least the soft threshold HT is the minimum possible value of an attribute. Node transmits data only when the value of that attributechanged by an amount equal to or greater than the STTx(Ni): Δ (SV) ≥ ST57
  57. 57. TEEN: Features & Discussion Good for time-critical applications Energy saving Less energy than proactive approaches Transmission consumes more energy than sensing Inappropriate for periodic monitoring Ambiguity between packet loss and unimportantdata (indicating no drastic change) The ST can be varied, depending on thecriticality/accuracy required58
  58. 58. APTEEN (Adaptive Threshold sensitive EnergyEfficient Network protocol) Extends TEEN to support both periodic sensing &reacting to time critical events Unlike TEEN, a node must sample & transmit a data ifit has not sent data for a time period equal to CT(count time) specified by CH Network lifetime: TEEN ≥ APTEEN ≥ LEACH Drawbacks of TEEN & APTEEN Overhead & complexity of forming clusters in multiplelevels and implementing threshold-based functions59
  59. 59. 60TEEN: Hierarchical vs. flattopologiesJamal N. Al-karaki, Ahmed E. Kamal,” Routing Techniques InWIRELESS SENSOR NETWORKS: A SURVEY”, IEEE Wireless Communications • December 2004
  60. 60. M.J. Handy, M. Haas, D. Timmermann, “Low Energy AdaptiveClustering Hierarchy with Deterministic Cluster-HeadSelection”, Fourth IEEE Conference on Mobile and WirelessCommunications Networks, Stockholm, September 2002LEACH: Low Energy Adaptive ClusteringHierarchy
  61. 61. LEACH:Phases Cluster-based approach The LEACH network has two phases: the set-up phase and the steady-state The Set-Up Phase Where cluster-heads are chosen The Steady-State The cluster-head is maintained Nodes transmit to cluster-head62
  62. 62. LEACH:The Cluster-Head The LEACH Network is made up of nodes, some of which are calledcluster-heads The job of the cluster-head is to collect data from theirsurrounding nodes and pass it on to the base station LEACH is dynamic because the job of cluster-head rotates Cluster-heads can be chosen stochastically If n < T(n), then that node becomes a cluster-head63
  63. 63. LEACH:An Example While neither ofthese diagrams is theoptimum scenario,the second is betterbecause the cluster-heads are spacedout and the networkis more properlysectioned64
  64. 64. S. Lindsey, C.S.Raghavendra, “PEGASIS: Power EfficientGathering in Sensor Information Systems”, Proceedings ofIEEE ICC 2001, pp. 1125-1130, June 2001Power-Efficient GAthering for SensorInformation Systems
  65. 65.  An enhancement over the LEACH Minimize distance nodes must transmit Minimize number of leaders that transmit toBS Minimize broadcasting overhead Distribute work more equally among allnodes increase the lifetime of each node by usingcollaborative techniquesPEGASIS66
  66. 66.  Greedy Chain Algorithm:1. Start with node furthest away from BS2. Add to chain closest neighbor to this node thathas not been visited3. Repeat until all nodes have been added to chain4. Constructed before 1st round of communicationand then reconstructed when nodes die Data fusion at each node (except end nodes) Only one message is passed at every node Delay calculation: N units for an N-nodenetwork Sequential transmission is assumed Node i (mod N) is the leader in round iPEGASIS:Greedy Chain Algorithm67
  67. 67. PEGASIS:Illustration68
  68. 68. PEGASIS: Drawbacks: Assumes that each sensor node is able tocommunicate with the BS directly Assumes that all sensor nodes have the same level ofenergy and are likely to die at the same time The single leader can become a bottleneck. Excessive data delay69
  69. 69.  Extension of PEGASIS Decrease the delay for the packets during transmission tothe base station Simultaneous transmissions of data messagesHierarchical PEGASIS70
  70. 70.  Another extension of PEGASIS The sensing area, centered at the BS, iscircularized into several concentric cluster levels. For each cluster level a node chain is constructed Farthest to nearest multi-hop and leader-by-leaderdata propagation (S. M. Jung, Y. J. Han, and T. M. Chung, “The Concentric ClusteringScheme for Efficient Energy Consumption in the PEGASIS,”Proceedings of the 9th International Conference on AdvancedCommunication Technology, Vol. 1, pp. 260-265, 2007)Enhanced PEGASIS71
  71. 71. REAR:Algorithm Assumptions:1. All sensor nodes are static and homogeneous afterdeployment.2. The communication links are symmetric.3. Each sensor node has several power levels whichthey can adjust.4. Each sensor node can know the distance to itsneighbors and to the BS.5. There is no obstacle between nodes.72
  72. 72. References[1] Ming Liu, Yuan Zheng, Jiannong Cao, Guihai Chen, Lijun Chen,Haigang Gong, “AnEnergy-Aware Protocol for Data Gathering Applications in Wireless SensorNetworks”, IEEE Communications Society subject matter experts for publication inthe ICC 2007 proceedings[2] Jones, Sivalingam, Agrawal, and Chen survey article in ACM WINET, July 2001;[3] Lindsey, Sivalingam, and Raghavendra book chapter in Wiley Handbook of MobileComputing, Ivan Stojmenovic, Editor, 2002.[4] C. Intanagonwiwat, R. Govindan and D. Estrin, “Directed Diffusion: A scalableand robust communication paradigm for sensor networks”, IEEE/ACMMobicom, 2000[5] A. Manjeshwar , D. P. Agarwal, “TEEN: a Routing Protocol for EnhancedEfficiency in Wireless Sensor Networks,” 1st Int’l. Wksp. on Parallel and Distrib.Comp. Issues in WirelessNetworks and Mobile Comp., 2001[6] M.J. Handy, M. Haas, D. Timmermann, “Low Energy Adaptive ClusteringHierarchy with Deterministic Cluster-Head Selection”, Fourth IEEE Conferenceon Mobile and Wireless Communications Networks, Stockholm, September 2002[7] S. Lindsey, C.S.Raghavendra, “PEGASIS: Power Efficient Gathering in SensorInformation Systems”, Proceedings of IEEE ICC 2001, pp. 1125-1130, June 200173

×