Survey on Coverage Problems in Wireless Sensor Networks

1. Survey on Coverage Problems in Wireless Sensor Networks M. T. Thai, F. Wang, H. Du, and X. Jia, "Coverage Problems in Wireless Sensor Networks: Designs and Analysis," International Journal of Sensor Networks, special issue on Coverage Problems in Sensor Networks, vol. 3, no. 3, pp. 191-200, 2008 Presented By Donghyun KimJuly 10, 2008Mobile Computing and Wireless Networking Research Group at University of Texas at Dallas

2. Sensor Coverage Problem • Area vs. Target Coverage Problem • Notations and Definitions • Maximum Lifetime Coverage Problem • Centralized Algorithms • Distributed and Localized Algorithms • Future Topics • k-Coverage Problem • Connected Coverage and Connected k-Coverage Problem • Minimum Coverage Breach Agenda Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

3. Communication Range Sensor Node Sensing Range Sensing and Communication Range Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

4. Basically, it is a scheduling problem. T Sensor Coverage Problem Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

5. T Area vs. Target Coverage Problem Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

6. Target Coverage vs. Set Cover Problem Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

7. We have sensors . • A sensor’s state can be transmit, receive, idle, or sleep. • The lifetime of is unit time. • is the sensing range of . • is the transmission range of . Notations Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

8. Definition 1: Sensor Covering a Point • A sensor is said to cover a point iff the distance . • Definition 2: Sensor Covering an Area • A sensor is said to cover an area iff for every point in , covers . • Definition 3: Direct Communication • Sensor and can communicate directly with each other iff and . • Definition 4: Communication Graph • Given a sensor network consisting of n sensors, the communication graph of the network is an undirected graph , where is a set of sensors and is a set of edges consisting edges between any two sensors that can communicate directly with each other. • It is a Disk Graph with Bidirectional edges (DGB) – DG ignoring unidirectional edges. Definitions Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

9. Definition 5: Maximum Lifetime Coverage • Given a network consisting of sensors and an interest region, find a family of ordered pairs , where is a set of sensors that completely cover the interest region and is the time duration for to be active such that to maximize . Each sensor appears in with a total time at most . Definitions Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

10. For example, suppose four sensors and 2 targets . Given , we can find the family of ordered pairs . Definitions – cont’ Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

11. Definition 6: Connected Coverage Problem • Given a sensor network consisting of sensors and an interest region, find a family of ordered pairs satisfying the following two conditions. • Satisfy the conditions of the Maximum Lifetime Coverage problem • The communication graph induced by is connected • Definition 7: -Coverage Problem • Given a sensor network consisting of sensors and an interest region, find a family of ordered pairs satisfying the following two conditions. • Satisfy the conditions of the Maximum Lifetime Coverage problem • Each point in is covered by at least distinct sensors in each . • Definition 8: Connected -Coverage Problem • Given a sensor network consisting of n sensors and an interest region, find a family of ordered pairs satisfying the following two conditions. • Satisfy the conditions of the -Coverage problem • The communication graph induced by is connected Definitions – cont’ Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

12. Given MAC Layer cannot handle the collision problem, the size of a subset has to be no more than some bandwidth constraint . Otherwise, coverage breach may occurs (i.e. some report can be discarded.) • Definition 9: Minimum Coverage Breach • Given a sensor network consisting of sensors and an interest region, find a family of ordered pairs such that the total coverage breach is minimum. • Theorem1 • The Maximum Lifetime Coverage problem, the Connected Coverage problem, the -Coverage problem, the Connected -Coverage problem, and the Minimum Coverage Breach are NP-complete. Definitions – cont’ Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

13. Definition 10: Field • A field is a set of all points such that they are covered by the sane set of sensors. • Definition 11: Critical Element • A critical element is a field covered by a minimal number of sensors. • The authors in [17] modeled the area coverage problem as the maximum number of disjoint sets problem. • Divide a given interest region into a set of fields. • An heuristic algorithm, Most Constrained-Minimally Constraining Covering, is executed to compute the maximum number of disjoint sets in following ways: • Cover a high number of uncovered fields. • Cover sparsely covered fields. • Do not cover fields redundantly. • Time complexity is . Assumes for all . [17] S. Slijepcevic and M. Potkonjak, Power Efficient Organization of Wireless Sensor Networks, IEEE International Conference on Communications, June 2001. Maximum Lifetime CoverageCentralized Algorithms Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

14. The authors in [18] modeled the coverage problem as the maximum number of disjoint dominating sets in an undirected graph , where is a set of sensors and is a set of edges such that iff and are within other’s sensing range. • A graph-coloring based two phase approximation algorithm is proposed. • Dominating set does not guarantee the complete coverage. • [18] has 1.5 to 2 times longer lifetime than [17]. • The authors in [16] reduce the target coverage problem to a maximum flow problem and formulate it as a mixed integer programming. • The result of [16] is slightly better than that of [17]. [16] M. Cardei and D.-Z. Du, Improving Wireless Sensor Network Lifetime through Power Aware Organization, ACM Wireless Networks, Vol. 11, No. 3, 2005. [18] M. Cardei, D. MacCallum, X. Cheng, M. Min, X. Jia, D. Li, and D.-Z. Du, Wireless Sensor Networks with Energy Efficient Organization, J. of Interconnection Networks, vol 3, No 3-4, pp 213~229, 2002. Maximum Lifetime CoverageCentralized Algorithms – cont’ Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

15. The authors in [13] and [15] found that organizing sensors into a maximum number of non-disjoint set covers may obtain a longer network lifetime. • Example • Disjoint set cover - and . Then total lifetime is 1.0 unit time. • Non-disjoint set cover – , and each set is activated for 0.5 unit time. Total lifetime is 1.5 unit time. • Find a set of set covers first and decide their activation time. [13] P. Berman, G. Calinescu, C. Shah, and A. Zelikovsky, Power Efficient Monitoring Management in Sensor Networks, Proceedings of WCNC '04, March 2004. [15] M. Cardei, M. T. Thai, Y. Li, and W. Wu, Energy-Efficient Target Coverage in Wireless Sensor Networks, Proceedings of the 24th conference of the IEEE Communications Society (INFOCOM), 2005. Maximum Lifetime CoverageCentralized Algorithms – cont’ Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

16. The authors in [13] used a packing Linear Programming technique. • Denote the number of set covers. Then, the coverage problem is formulated as follows. • and represent the index number of each sensor and each set cover, respectively. • For any e > 0, based on the -approximation Garg-Konemann algorithm in [7], -approximation algorithm is proposed. [7] N. Garg and J. Konemann, Faster and Simpler Algorithms for Multicommodity Flow and Other Fractional Packing Problems, Proc. of FOCS, 1997 [13] P. Berman, G. Calinescu, C. Shah, and A. Zelikovsky, Power Efficient Monitoring Management in Sensor Networks, Proceedings of WCNC '04, March 2004. Coverage Condition? Maximum Lifetime CoverageCentralized Algorithms – cont’ Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

17. The authors in [15] modeled this problem as Maximal Set Covers (MSC) as follows. • is a set of all sensors that can cover a target . • Assume for all sensors. • The above formulation is relaxed to an Linear Programming and solved using Ye’s algorithm in [8]. The complexity of this solution is quite high: • The authors in [15] also introduced a much faster heuristic algorithm whose complexity is , where is the number of targets and is the number of sensors that covers the most sparsely covered targets. [8] Y. Ye, An Potential Reduction Algorithm for Linear Programming, Mathematical Programming, Vol 50, pp 239-258, 1991. [15] M. Cardei, M. T. Thai, Y. Li, and W. Wu, Energy-Efficient Target Coverage in Wireless Sensor Networks, Proceedings of the 24th conference of the IEEE Communications Society (INFOCOM), 2005. Maximum Lifetime CoverageCentralized Algorithms – cont’ Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas

18. The authors in [19] proposed a localized algorithm to solve the area coverage problem, called Node Scheduling Scheme Based On Eligibly rule (SBO). • Several sensors cover the same area. • A sensor can be off when its sensing area is covered by its neighbors, called sponsors. • A (random) backoff-based scheme was introduced to avoid blind point (i.e. two nodes expect that the other will be its sponsor). • The nodes have to be synchronized but this part is not mentioned clearly. • The author in [14] proposed two distributed and localized approximation algorithms for the target coverage problem. • Convert a target cover problem instance to the problem of computing maximum number of disjoint dominating set problem (MDS) on a bipartite graph. • To be continued… [14] M. T. Thai, Y. Li, F. Wang, and D.-Z. Du, O(log n)-Localized Algorithms on the Coverage Problem in Heterogeneous Sensor Networks, 26th IEEE International Performance Computing and Communications Conference (IPCCC 2007), New Orleans, LA, April 11-13, 2007. [19] D. Tian and N. D. Georganas, A Coverage Preserving Node Scheduling Scheme for Large Wireless Sensor Networks, in Proceeding of the 1st ACM Workshop on Wireless Sensor Networks and Applications, 2002. Maximum Lifetime CoverageDistributed and Localized Algorithms Presented by Donghyun Kim on July 10, 2008Mobile Computing and Wireless Networking Research Group at The University of Texas at Dallas