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Bounded relay hop mobile data gathering in wireless sensor networks

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Presentation on theme: "Bounded relay hop mobile data gathering in wireless sensor networks"— Presentation transcript:

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 Introduction tradeoff Employing mobile collectors
Low energy consumption tradeoff Energy saving Collection latency 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 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 Static data sink Sensor Polling point d-hop bound Mobile collector tour Relay routing path

9 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 3 energy_unit/packet 4 energy_unit/packet 2-hop bound 3 energy_unit/packet 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
Iteration 1 6 20 5 16 14 24 15 2 17 21 7 8 d-hop = 2-hop 1 25 19 18 11 3 13 10 23 22 12 9 4

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

20 Centralized Algorithm for BRH-MDC Problem
Final result 6 20 5 16 14 24 15 2 17 21 7 8 d-hop = 2-hop 1 25 19 11 18 3 13 10 23 22 12 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 TENTA_PP.ID TENTA_PP.d_Nbrs TENTA_PP.Hop Node identification The number of its d-hop neighbors The minimum hop count of the tentative PP to the data sink

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

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

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

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

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

30 Performance Evaluation
Performance of SPT-DCA and PB-PSA Increasing relay hop bound d L佈建範圍邊長 N感測器數 Rs傳輸半徑

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

32 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.

33 SHDG scheme sensor Candidate polling point

34 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 4 6

35 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.

36 CME scheme

37 CME scheme

38 Performance Evaluation
Comparison with SHDG and CME

39 200 m CME 200 m

40

41 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

42 Thank you very much~

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