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Bounded relay hop mobile data gathering in wireless sensor networks

Bounded relay hop mobile data gathering in wireless sensor networks. Miao Zhao and Yuanyuan Yang Stony Brook University, New York. IEEE TRANSACTIONS ON COMPUTERS, VOL. 61, NO. 2, FEBRUARY 2012. Outline. Introduction Goal BRH-MDC Problem Centralized Algorithm for BRH-MDC Problem

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Bounded relay hop mobile data gathering in wireless sensor networks

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  1. Bounded relay hop mobile data gathering in wireless sensor networks Miao Zhao and Yuanyuan Yang Stony Brook University,New York IEEE TRANSACTIONS ON COMPUTERS, VOL. 61, NO. 2, FEBRUARY 2012

  2. Outline • Introduction • Goal • BRH-MDC Problem • Centralized Algorithm for BRH-MDC Problem • Distributed Algorithm for BRH-MDC Problem • Performance Evaluation • Conclusion

  3. Introduction • Data gathering in WSN • Multi-hop relay • High energy consumption 300 sensors deployed over a 300 m * 300 m field. Relay routing along shortest paths with minimum hop counts

  4. Introduction • Employing mobile collectors Mobile data gathering by visiting each sensor and static data sink. It will take the mobile collector about 66.9 minutes on the tour when it moves at an average speed of 1 m/s.

  5. Energy saving tradeoff Collection latency Introduction • Employing mobile collectors • Low energy consumption • High collection latency

  6. Goal • Proposing a polling-based approach that pursues a tradeoff between the energy saving and data collection latency • Achieves a balance between the relay hop count for local data aggregation and the moving tour length of the mobile collector.

  7. BRH-MDC Problem • Network assumption • The mobile collector has the freedom to move to any place in the sensing field

  8. Polling point Sensor Static data sink d-hop bound Relay routing path Mobile collector tour BRH-MDC Problem • Basic idea • Find a set of special nodes referred to as polling points (PPs) in the network • The PPs are compactly distributed and close to the data sink. • The number of the PPs is the smallest

  9. 4 energy_unit/packet 3 energy_unit/packet 2-hop bound 3 energy_unit/packet BRH-MDC Problem • Relay hop count should be bounded ( d-hop ) • A sensor network may expect to achieve a certain level of systematic energy efficiency. Eg. If each transmission costs one unit of energy and the energy efficiency of 0.33 packet/energy_unit is expected • The bound is necessary due to buffer constraint on the sensors.

  10. BRH-MDC Problem Formulation

  11. BRH-MDC Problem Formulation PP i PP u

  12. BRH-MDC Problem Formulation PP u Layer =1 Layer =2 PP u i j

  13. BRH-MDC Problem Formulation PP u Layer =0

  14. BRH-MDC Problem Formulation PP u v v PP PP u v PP PP

  15. BRH-MDC Problem Formulation PP Sink u π PP PP PP PP 3 u 2

  16. Outline • Introduction • Goal • BRH-MDC Problem • Centralized Algorithm for BRH-MDC Problem • Distributed Algorithm for BRH-MDC Problem • Performance Evaluation • Conclusion

  17. Centralized Algorithm for BRH-MDC Problem • Shortest Path Tree based Data Collection Algorithm (SPT-DCA) • Energy saving and data collection latency • Constraint of the relay hop bound (d-hop) • The sensors selected as the PPs are compactly distributed and close to the data sink. • The number of the PPs is the smallest under the constraint of the relay hop bound.

  18. Centralized Algorithm for BRH-MDC Problem 6 20 5 Iteration 1 14 16 24 15 2 17 21 d-hop = 2-hop 7 8 1 25 19 11 18 3 13 10 23 12 22 9 4

  19. = 1-hop Centralized Algorithm for BRH-MDC Problem 6 20 5 Iteration 2 14 16 24 15 2 17 21 d-hop = 2-hop 7 8 1 25 19 11 18 3 13 10 23 12 22 9 4

  20. Centralized Algorithm for BRH-MDC Problem 6 20 5 Final result 14 16 24 15 2 17 21 d-hop = 2-hop 7 8 1 25 19 11 18 3 13 10 23 12 22 9 4

  21. Outline • Introduction • Goal • BRH-MDC Problem • Centralized Algorithm for BRH-MDC Problem • Distributed Algorithm for BRH-MDC Problem • Performance Evaluation • Conclusion

  22. Distributed Algorithm for BRH-MDC Problem • Priority based PP selection algorithm (PB-PSA) • Energy saving and data collection latency • The primary parameter is the number of d-hop neighbors, which are the sensors in its d-hop range. • The secondary parameter is the minimum hop count to the data sink. TENTA_ PP The number of its d-hop neighbors Node identification The minimum hop count of the tentative PP to the data sink

  23. TENTA_ PP =3 TENTA_ PP =3 TENTA_ PP =3 TENTA_ PP =3 Update TeENTA_PP.Hop Rule 1 : Choose the neighbor with maxiumTENTA_PP.d_Nbrs • Priority based PP selection algorithm (PB-PSA) Round 1 d-hop=2-hop 1 2 3 6 TENTA_ PP =4 TENTA_ PP 4 5 TENTA_ PP = 5 TENTA_ PP =4 TENTA_ PP = 5,4,6

  24. TENTA_ PP =3 TENTA_ PP =3 TENTA_ PP =3 TENTA_ PP =3 Update TeENTA_PP.Hop Rule 2 : Choose the neighbor with minimum TENTA_PP.Hop • Priority based PP selection algorithm (PB-PSA) Round 2 d-hop=2-hop 1 2 3 6 TENTA_ PP =3 TENTA_ PP =4 TENTA_ PP 4 5 TENTA_ PP =4 TENTA_ PP =4,3 TENTA_ PP =3

  25. TENTA_ PP =3 TENTA_ PP =3 • Priority based PP selection algorithm (PB-PSA) 1 2 3 6 TENTA_ PP =3 Declar TENTA_ PP =3 4 5 TENTA_ PP =3 TENTA_ PP =3

  26. PP =3 PP =3 PP =3 PP =3 PP =3 • Priority based PP selection algorithm (PB-PSA) 1 2 3 6 Declar 4 5

  27. Round = 1 TENTA_ PP =2 TENTA_ PP =3 TENTA_ PP =4 TENTA_ PP =5 TENTA_ PP =5 Round =2 TENTA_ PP =2 TENTA_ PP =3 TENTA_ PP =2 TENTA_ PP =4 TENTA_ PP =5 TENTA_ PP =5 TENTA_ PP =5 TENTA_ PP =5 TENTA_ PP =5 TENTA_ PP =5 • Priority based PP selection algorithm (PB-PSA) Hop count +random time duration d-hop=2-hop 2 4 1 3 5 TENTA_ PP =1 TENTA_ PP =2 TENTA_ PP =3 TENTA_ PP =4 TENTA_ PP =5

  28. Performance Evaluation • Simulation Parameter • A network with 30 sensors scattered over a 70m x 70m square area. • d is set to 2.(2-hop bound)

  29. Performance Evaluation • Performance of SPT-DCA and PB-PSA • Increasing relay hop bound d

  30. Performance Evaluation • Performance of SPT-DCA and PB-PSA • Increasing transmission range Rs d=3 d=2 d=2 d=3

  31. Performance Evaluation • Authors: M. Ma and Y. Yang • University: State University of New York, USA • Paper: “Data Gathering in Wireless Sensor Networks with Mobile Collectors” • Published from: IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2008.

  32. SHDG scheme sensor Candidate polling point

  33. SHDG scheme :The cost of an uncovered neighbor set S and equal to the shortest distance between S and any covered neighbor set. : denote the average cost to cover each uncovered sensor in S. =d1/3 6 4

  34. Performance Evaluation • Authors: D. Jea, A.A. Somasundara and M.B. Srivastava • University: University of California, Los Angeles • Title: “Multiple Controlled Mobile Elements (Data Mules) for Data Collection in Sensor Networks” • From: IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), 2005.

  35. CME scheme

  36. CME scheme

  37. Performance Evaluation • Comparison with SHDG and CME

  38. 200 m CME 200 m

  39. Conclusion • The paper have studied mobile data gathering in wireless sensor networks • The relay hop count of sensors for local data aggregation • The tour length of the mobile collector • Then presented two efficient algorithms to give practically good solutions. • The results demonstrate that the proposed algorithms can greatly shorten the data collection tour length with a small relay hop bound

  40. Thank you very much~

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