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

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Presentation on theme: "An Integrated Approach to Sensor Role Selection by Mark Perillo and Wendi Heinzelman."— 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 Example for Power reduction Energy CostMICAzTelosB Compute for 1 T clk 3.5 nJ (1)1.2 nJ(1) Transmit 1 bit0.60 μJ(170)0.72 μJ(600) Receive 1 bit0.67 μJ(190)0.81 μJ(680) Listen for 1 T clk 9.2 nJ(3)15.0 nJ(13) Sleep for 1 T clk 3 pJ(10 -3 )9 pJ(10 -2 ) Operation System: eg. TinyOS to put microcontroller to sleep; to put radio to sleep Low-level Protocol: low-level listening on physical layer; SMAC TMAC on link-layer; Reduce idle listening, overheads (collision, overhearing, protocol overheads)

5 BACKGROUND Abundant data Filter sensors Multi-hopping Design routing Diverse importance Assign duties

6

7 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

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

9 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

10 Relevant Work – Routing Protocols( II ) Principle: considering energy efficiency Power-aware MAC layer routing route through nodes with sufficient remaining power route through lightly-loaded nodes Maximizing the network lifetime minimize the energy consumed every packet

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

12 Critical Nodes T h e s e n s o r s i n t h e s p a r s e s t r e g i o n s

13 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

14 MODELING -- Varianbles

15 MODELING – Formalization( I ) Coverage : Number of nodes: N t = N s + N sink Data flow:

16 MODELING – Formalization( II ) Energy consumption: Scheduling: Lifetime:

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

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

19 E(Xa) = 2; E(Xb) = 3; E(Xc) = 2; E(Xd) = 1; C wc (S1) = ½; C wc (S2) = ½; C wc (S3) = 1;

20 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.

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

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

23 SOLUTION – DAPR (Distributed Activation with Predetermined Routers) The decision made in sensor selection and route discovery are influenced each other. Procedure: Route Discovery Phase Sensor Selection Phase Sensor query

24 DAPR – Route Discovery Phase( I ) Assumption: Nodes have location information of neighbors with redundant coverage regions; Low power wakeup system Cost of a link = routing cost nodei x energy for transmission + routing cost nodej x energy for reception Cost of a route = sum of links in the route

25 DAPR – Route Discovery Phase( II ) Data SinkNode Initiate query flood queryreceive query Calculate link cost Update query packet forward query (delay scheme): proportional to C link (S i ) Next Node

26 DAPR – Sensor Selection Phase * Initial: be inactive to sense and generate data Assign activation delay (proportional to the route cost) Check received activation beacon check if the neighborhood is already covered Send activation beacon send activation beacon to neighbors if possible (Send deactivation beacon) send deactivation beacon if in high redundancy and in the highest cost route * DAPR: A Protocol for Wireless Sensor Networks Utilizing an Application-based Routing Cost

27 DAPR (Cont’d) Awareness of neighbors Location Redundant coverage Given highest priority Nodes along highest route Reserving opt-out/deactivation beacon Sending beacon In single hop (D transmission_range >> D sensing_range ) Forwarding (no guarantee)

28 SIMULATION&ANALYSIS – Simulation result

29 SIMULATION&ANALYSIS – Experiment Result deploymentC(S i )1C ea (S i )C wc (S i )C cc (S i ) Uniform 100% coverage % coverage Clustered 100% coverage % coverage Video 100% coverage % coverage

30 SIMULATION&ANALYSIS – Experiment Result for Sensor Selection Configuring activation/backoff delay in Worst Coverage-Coverage Routing Cost Selection Criteria RandomIndividual CostCumulative Routing Cost Uniform Clustered Video

31 SIMULATION&ANALYSIS – Experiment Result for Combing routing cost Worst coverage-based + energy-aware routing cost β0 C wc C ea Uniform Clustered Video U + routers C + routers V + routers

32 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

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

34 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.

35 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.

36 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

37 Thank You


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