1 / 36

Enhancing Wireless Sensor Networks with Sensor Role Selection and Energy-Efficient Routing

Explore a comprehensive approach to sensor role selection and energy-efficient routing in Wireless Sensor Networks (WSNs). The proposed model, DAPR (Distributed Activation with Predetermined Routers), balances energy consumption, redundancy reduction, and data coverage to optimize network performance and longevity.

galeno
Download Presentation

Enhancing Wireless Sensor Networks with Sensor Role Selection and Energy-Efficient Routing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Integrated Approach to Sensor Role Selection by Mark Perillo and Wendi Heinzelman

  2. Outline • Motivation • Background • Modeling • Solution (DAPR) • Analysis • Comparison • Conclusion

  3. 1. MOTIVATION (WSN Energy Efficiency) • Limited energy supply VS long network lifetimes • Hardware, Operating System, Low-level protocol design • Balance/reduce energy consumption • Reduce redundancy but ensure QoS requirement • dynamic sensor selection, in-network aggregation, distributed source coding

  4. BACKGROUND • Abundant data • Filter sensors • Multi-hopping • Design routing • Diverse importance • Assign duties

  5. WHAT HAVE BEEN DONE?

  6. Relevant Work -- Sensor Selection(Ｉ) • Principle : desired coverage • PEAS • activeness probing/querying; • Gur game paradigm • state switching according to base station • Sensing coverage protocol • sleep/wake time scheduling according to neighbors’ with differentiated surveillance • neighbor redundancy, coverage redundancy • CCP • coverage and connectivity

  7. Relevant Work -- Sensor Selection(ＩＩ) • Principle: Considering routing • less sensors + some routings + short path = desired coverage

  8. Relevant Work – Routing Protocols(Ｉ) • Principle: Shortest path • Table-driven routing protocol • destination-sequenced distance vector routing • clusterhead gateway switch routing • the wireless routing protocol • Source-initiated on-demand routing • ad hoc on-demand distance vector routing • dynamic source routing • temporally ordered routing algorithm • associativity-based routing • signal stability routing

  9. Relevant Work – Routing Protocols(ＩＩ) • Principle: considering energy efficiency • Power-awareMAClayerrouting • routethroughnodeswithsufficientremainingpower • routethroughlightly-loadednodes • Maximizingthenetworklifetime • minimizetheenergyconsumedeverypacket

  10. PROPOSAL Sensor selection + Energy conserved routing PRINCIPLE To use the sensors not as important as data generators more liberally as routers

  11. CriticalNodesThesensorsinthesparsestregions

  12. MODELING – Assumptions • Power consumption largely from traffic transmitted and received • Portion or entirety of an area A needs to be monitored by any one or multiple sensors • There may be one or several data sink locations

  13. MODELING -- Varianbles

  14. MODELING – Formalization(Ｉ) Coverage: Number of nodes: Nt = Ns + Nsink Data flow:

  15. MODELING – Formalization(ＩＩ) Energy consumption: Scheduling: Lifetime:

  16. MODELING – Coverage-Aware Routing Cost Common cost (energy-aware cost): Total energy in a subset area x:

  17. MODELING – Worst Coverage-Based Cost Finds out the least-covered subregion

  18. E(Xa) = 2; E(Xb) = 3; E(Xc) = 2; E(Xd) = 1; Cwc(S1) = ½; Cwc(S2) = ½; Cwc(S3) = 1;

  19. MODELING – Comprehensive Coverage-Based Cost Weighted sum (in terms of area of subregion) of 1/E(x) It provides a more balanced view of a nodes importance to the sensing task.

  20. E(Xa) = 2; E(Xb) = 3; E(Xc) = 2; E(Xd) = 1; Ccc(S1) = area(A)/2 + area(B)/2 Ccc(S2) = area(A)/2 + area(B)/3+are(C)/2 Ccc(S3) = area(B)/3 + area(C)/2+are(D)/1

  21. MODELING – Combining Cost Functions Most effective in extending lifetime with 100 percent coverage: Effective in providing long network lifetimes with graceful degradation:

  22. SOLUTION – DAPR (Distributed Activation with Predetermined Routers) Thedecisionmadeinsensorselectionandroutediscoveryareinfluencedeachother. Procedure: RouteDiscoveryPhase SensorSelectionPhase Sensorquery

  23. DAPR–RouteDiscoveryPhase(Ｉ) • Assumption: • Nodeshavelocationinformationofneighborswithredundantcoverageregions; • Lowpowerwakeupsystem Costofalink=routing cost nodei xenergyfortransmission+routing cost nodej xenergyforreception Costofaroute=sumoflinksintheroute

  24. DAPR–RouteDiscoveryPhase(ＩＩ) DataSink Node Initiatequery floodquery receivequery Calculatelinkcost Updatequerypacket forwardquery(delayscheme):proportionaltoClink(Si) Next Node

  25. DAPR–SensorSelectionPhase* Initial:beinactivetosenseandgeneratedata • Assignactivationdelay(proportional to the route cost) • Checkreceivedactivationbeacon • checkiftheneighborhoodisalreadycovered • Sendactivationbeacon • sendactivationbeacontoneighborsifpossible • (Senddeactivationbeacon) • senddeactivationbeaconifinhighredundancyandinthehighestcostroute • * DAPR: A Protocol for Wireless Sensor Networks Utilizing an Application-based Routing Cost

  26. DAPR(Cont’d) • Awarenessofneighbors • Location • Redundantcoverage • Givenhighestpriority • Nodesalonghighestroute • Reservingopt-out/deactivationbeacon • Sendingbeacon • Insinglehop(Dtransmission_range>>Dsensing_range) • Forwarding(noguarantee)

  27. SIMULATION&ANALYSIS – Simulation result

  28. SIMULATION&ANALYSIS – Experiment Result

  29. SIMULATION&ANALYSIS – Experiment Result for Sensor Selection Configuring activation/backoff delay in Worst Coverage-Coverage Routing Cost

  30. SIMULATION&ANALYSIS – Experiment Result for Combing routing cost Worst coverage-based + energy-aware routing cost

  31. COMPARISON – to Centralized Approach • Assuming subject to these conditions: data flow, energy consumption constraints and scheduling constraints, we try to maximize the operation time of the system

  32. COMPARISON – to Centralized Approach Uniform scenario: worst coverage + energy-aware cost with DAPR gains 14% over the nonintegrated approach. 56% closer to centralized solution.

  33. COMPARISON – to Centralized Approach (Cond’t) In clustered scenario: Worst coverage routing cost with DAPR improves lifetime by 56%. 77% closer to centralized solution, Due to use of the coverage-aware routing cost.

  34. COMPARISON – to Centralized Approach (Cond’t) In video scenario: DAPR with the combined worst coverage and energy-aware cost makes lifetime gain 50%,Closing gap by 76%, Because the selection of sensors based on the cumulative route cost.

  35. CONCLUSION Contribution Incorporation of coverage information into the routing protocol and the priority for sensor selection Worst coverage-based cost – maintaining 100% coverage for the maximum lifetime Comprehensive coveraged-based cost – giving a more balanced interpretation of a node’s value to the sensing task

  36. Thank You

More Related