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Connectivity and Scheduling in Wireless Sensor Networks

Connectivity and Scheduling in Wireless Sensor Networks. Youn-Hee Han yhhan@kut.ac.kr Korea University of Technology and Education Internet Computing Laboratory http://icl.kut.ac.kr. Connectivity. Connectivity. Why Connectivity?

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Connectivity and Scheduling in Wireless Sensor Networks

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  1. Connectivity and Scheduling in Wireless Sensor Networks Youn-Hee Han yhhan@kut.ac.kr Korea University of Technology and EducationInternet Computing Laboratory http://icl.kut.ac.kr

  2. Connectivity

  3. Connectivity • Why Connectivity? • Any sensing data should be sent to gateway (sink, base station) node • Multi-hop routing Base Station Sink

  4. K-Connectivity • Connected Graph of Sensor Networks • Vertex: each sensor nodes • Edge: direct communication path for pairs of sensors • there exists an edge between two vertices iff the distance between them is less or equal to the transmission range r.

  5. K-Connectivity • [Definition]k-connectivity • The network will remain connected after removing any arbitrary k-1 sensors from network. • It is also called “vertex k-connectivity” (not “edge k-connectivity”) • k-connected:  any pair of nodes are connected by k indep. paths • Independent paths:

  6. K-Connectivity • Examples 2-connected 4-connected

  7. K-Edge-Connectivity • [Definition]k-edge-connectivity • The network will remain connected after removing any arbitrary k-1 edges from network. • k-edge-connected:  any pair of nodes are connected by k disjoint paths • disjoint paths:

  8. Min-Power Connectivity Problem • Connectivity & Transmission Power • Nodes in the network correspond to transmitters • More power  larger transmission range  More Edges  More Connectivity • transmitting to distance r requires rpower • Battery operated  power conservation critical • [Definition]Min-Power Connectivity Problems • Find min-power range assignment so that the resulting communication network satisfies prescribed properties (k-connectivity)

  9. e e d d f f c c g g b b a a Min-Power Connectivity Problem Range assignment Communication network

  10. K-Connectivity & K-Coverage • Relationbetween K-Coverage and K-Connectivity [3] • Communication Range: • Sensing Range: [Theorem] • If the given region is continuous and , “The region is k-covered” means “The region is k-connected” • For example, k=1 • Assume that the requested coverage level, k, is one and • If The sensors covers the whole region completely, then • Any sensing data produced by a sensor can be delivered to the sink node.

  11. Sensing and Communication Ranges • Real Products’ Ranges [7]

  12. Coverage and Surveillance Path [Voronoi Diagram Tutorial] http://nms.lcs.mit.edu/~aklmiu/6.838/L7.pdf

  13. Voronoi diagram • Voronoi diagram [8] • The Voronoi diagram is formed from lines that bisect and are perpendicular to the lines that connect two neighboring sensors. • Each point s has a Voronoi cellV(s) consisting of all points closer to s than to any other point

  14. Voronoi diagram • Voronoi diagram examples • 1 point • 2 points form “a perpendicular bisector”

  15. Voronoi diagram • Voronoi diagram examples • Collinear points form “a series of parallel lines”

  16. Voronoi diagram • Voronoi diagram examples • Non-collinear points form “Voronoi half lines” that meet at a vertex

  17. Voronoi diagram • Voronoi cells and segments • Which of the following is true for 2-D Voronoi diagrams? • Four or more non-collinear pointss are…1) sufficient to create a bounded cell2) necessary to create a bounded cell3) 1 and 24) none of above • Four points’ degenerate caseof bounded cell:

  18. Property I of Voronoi diagram

  19. Property II of Voronoi diagram

  20. Surveillance Path • Maximal Breach Path [8] • Voronoi Path (= Maximal Breach Path) • The path where the surveillance level is the lowest • The path where its closest distance to any sensor is as large as possible. Voronoi Path (Edge) Voronoi diagram Voronoi Partition

  21. Surveillance Path • Maximal Support Path [8] • Delaunay Triangulation Path (= Maximal Support Path) • The path where the surveillance level is the lowest • The path where its closest distance to any sensor is as short as possible. Delaunay triangulation

  22. Coverage and Scheduling

  23. Scheduling • Basic Policy • Sensor should be active or sleep? • Scheduling (related to the coverage issue) • An interval: is active • Another interval: is active • So, the battery power can be saved

  24. Scheduling • Scheduling Type • Centralized • All sensors send “their location information” to the centralized sink node. • The sink node performs “its scheduling algorithm” for the sensors • The sink node broadcasts “the scheduling information” to all sensor nodes • Each sensor becomes active or sleep according to the information • Distributed • Each sensor self-determies its scheduling time • # of messages reduced

  25. Centralized Scheduling • MDSC (Maximum Disjoint Set Covers) [9] [Definition] Maximum Disjoint Set Covers Problem

  26. Centralized Scheduling • MDSC (Maximum Disjoint Set Covers) [9] • For example, • C={S1, S2, S3, S4}, TARGETS={t1, t2, t3} • A sensor’s battery lifetime: 1 • Network Lifetime without any scheduling: 1 • By MDSCScheduling • Two Set Covers, C1 and C2 • C1={S1, S2} with active time=1 • C1={S3, S4} with active time=1 • So that, network lifetime: 2 t1 s1 s1 s3 t1 s2 s4 t2 t2 s3 t3 s2 t3 s4

  27. Centralized Scheduling • MSC (Maximum Set Covers) [10] [Definition] Maximum Set Covers Problem removed! MSC MDSC MDSC problem is a special case of MSC problem.!

  28. Centralized Scheduling • MSC (Maximum Set Covers) [10] • For Example, • By MSCScheduling • Network Lifetime: 2.5 t1 s1 s3 s4 t2 t3 s2 active time=0.5 active time=0.5 active time=1 active time=0.5

  29. Centralized Scheduling • Integer Programming Formulation of the MSC Problem [10]

  30. Centralized Scheduling • Integer Programming Formulation of the MSC Problem [10]

  31. Centralized Scheduling • Integer Programming  Linear Programming

  32. Centralized Scheduling • MSC (Maximum Set Covers) [10, 11] • Existing Algorithms • Linear Programming [10] • Greedy [10] (Complexity: ) • Branch-and-Bound [11] i: # of setcovers, m: # of targets, n: # of sensors

  33. Centralized Scheduling • MSC (Maximum Set Covers) [10, 11] • Existing Algorithms • Linear Programming [10] • Greedy [10] (Complexity: ) • Branch-and-Bound [11] i: # of setcovers, m: # of targets, n: # of sensors

  34. Distributed Scheduling • 1-Coverage Preserving Scheduling (1-CP) [12] • For Example Init Phase: 1) Each sensor exchange its location and Ref. value 2) Each sensor get its schedule (active) time The set of intersection points within ‘s area Trnd=20 The set of sensorscovering the target p Ref1=2, Ref2=9, Ref3=11

  35. Distributed Scheduling • 1-Coverage Preserving Scheduling (1-CP) [12] 2 16.5 5.5 9 11

  36. References • C.-F. Huang and Y.-C. Tseng, The Coverage Problem in a Wireless Sensor Network, In ACM International Workshop on Wireless Sensor Networks and Applications (WSNA), pp. 115–121, 2003. • N. Ahmed, S. S. Kanhere and S. Jha, Probabilistic Coverage in Wireless Sensor Networks, in Proceedings of the IEEE Workshop on Wireless Local Networks (WLN, in conjunction with LCN 2005) , Sydney, Australia, pp. 672-679, November 2005. • X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless, and C. Gill, Integrated coverage and connectivity configuration in wireless sensor networks, In ACM International Conf. on Embedded Networked Sensor Systems (SenSys), pp. 28–39, 2003. • C.-F. Huang, Y.-C. Tseng, and L.-C. Lo, The Coverage Problem in Three-Dimensional Wireless Sensor Networks, Journal of Interconnection Networks, Vol. 8, No. 3, pp. 209-227. Sep. 2007. • Y. Zou and K. Chakrabarty, "Sensor deployment and target localization based on virtual forces," in Proceedings of INFOCOM 2003, March 2003. • S.-P. Kuo, Y.-C. Tseng, F.-J. Wu, and C.-Y. Lin, A Probabilistic Signal-Strength-Based Evaluation Methodology for Sensor Network Deployment, International Journal of Ad Hoc and Ubiquitous Computing, Vol. 1, No. 1-2, pp. 3-12, 2005 36/37

  37. References • Honghai Zhang and Jennifer C. Hou, ``On deriving the upper bound of a-lifetime for large sensor networks,'' Proc. ACM Mobihoc 2004, June 2004 • S. Megerian, F. Koushanfar, G. Qu, G. Veltri, M. Potkonjak. "Exposure In Wireless Sensor Networks: Theory And Practical Solutions," Journal of Wireless Networks, Vol. 8, No. 5, ACM Kluwer Academic Publishers, pp. 443-454, September 2002 • M. Cardei and D.-Z. Du, "Improving Wireless Sensor Network Lifetime through Power Aware Organization," ACM Wireless Networks, Vol. 11, pp. 333-340, 2005. • M. Cardei, M. T. Thai, Y. Li, and W. Wu, "Energy-efficient Target Coverage in Wireless Sensor Networks," In IEEE Infocom 2005, vol. 3, pp. 1976-1984, 2005. • 김용환, 이헌종, 한연희, "무선 센서 네트워크 수명 연장을 위한 에너지 인지적 스케줄링 알고리즘," 한국정보과학회 학술발표논문집 2008년도 가을, 2008년 10월 • C.-F. Huang, L.-C. Lo, Y.-C. Tseng, and W.-T. Chen Decentralized Energy-Conserving and Coverage-Preserving Protocols for Wireless Sensor Networks, ACM Trans. on Sensor Networks, Vol. 2, No. 2, pp. 182-187, 2006. • V. Raghunathan, C. Schurgers, S. Park, and M. B. Srivastava, Energy-Aware Wireless Microsensor Networks, IEEE Signal Processing Magazine, 19 (2002), pp 40-50. 37/37

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