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An Integrated Approach to Sensor Role Selection. by Mark Perillo and Wendi Heinzelman. Outline. Motivation Background Modeling Solution (DAPR) Analysis Comparison Conclusion. 1. MOTIVATION (WSN Energy Efficiency). Limited energy supply VS long network lifetimes
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An Integrated Approach to Sensor Role Selection by Mark Perillo and Wendi Heinzelman
Outline • Motivation • Background • Modeling • Solution (DAPR) • Analysis • Comparison • Conclusion
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
BACKGROUND • Abundant data • Filter sensors • Multi-hopping • Design routing • Diverse importance • Assign duties
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
Relevant Work -- Sensor Selection(II) • Principle: Considering routing • less sensors + some routings + short path = desired coverage
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
Relevant Work – Routing Protocols(II) • Principle: considering energy efficiency • Power-awareMAClayerrouting • routethroughnodeswithsufficientremainingpower • routethroughlightly-loadednodes • Maximizingthenetworklifetime • minimizetheenergyconsumedeverypacket
PROPOSAL Sensor selection + Energy conserved routing PRINCIPLE To use the sensors not as important as data generators more liberally as routers
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
MODELING – Formalization(I) Coverage: Number of nodes: Nt = Ns + Nsink Data flow:
MODELING – Formalization(II) Energy consumption: Scheduling: Lifetime:
MODELING – Coverage-Aware Routing Cost Common cost (energy-aware cost): Total energy in a subset area x:
MODELING – Worst Coverage-Based Cost Finds out the least-covered subregion
E(Xa) = 2; E(Xb) = 3; E(Xc) = 2; E(Xd) = 1; Cwc(S1) = ½; Cwc(S2) = ½; Cwc(S3) = 1;
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.
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
MODELING – Combining Cost Functions Most effective in extending lifetime with 100 percent coverage: Effective in providing long network lifetimes with graceful degradation:
SOLUTION – DAPR (Distributed Activation with Predetermined Routers) Thedecisionmadeinsensorselectionandroutediscoveryareinfluencedeachother. Procedure: RouteDiscoveryPhase SensorSelectionPhase Sensorquery
DAPR–RouteDiscoveryPhase(I) • Assumption: • Nodeshavelocationinformationofneighborswithredundantcoverageregions; • Lowpowerwakeupsystem Costofalink=routing cost nodei xenergyfortransmission+routing cost nodej xenergyforreception Costofaroute=sumoflinksintheroute
DAPR–RouteDiscoveryPhase(II) DataSink Node Initiatequery floodquery receivequery Calculatelinkcost Updatequerypacket forwardquery(delayscheme):proportionaltoClink(Si) Next Node
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
DAPR(Cont’d) • Awarenessofneighbors • Location • Redundantcoverage • Givenhighestpriority • Nodesalonghighestroute • Reservingopt-out/deactivationbeacon • Sendingbeacon • Insinglehop(Dtransmission_range>>Dsensing_range) • Forwarding(noguarantee)
SIMULATION&ANALYSIS – Experiment Result for Sensor Selection Configuring activation/backoff delay in Worst Coverage-Coverage Routing Cost
SIMULATION&ANALYSIS – Experiment Result for Combing routing cost Worst coverage-based + energy-aware routing cost
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
COMPARISON – to Centralized Approach Uniform scenario: worst coverage + energy-aware cost with DAPR gains 14% over the nonintegrated approach. 56% closer to centralized solution.
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.
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.
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