1 / 20

Energy-Efficient Target Coverage in Wireless Sensor Networks

This paper addresses the critical issue of power scarcity in wireless sensor networks by proposing a Maximum Set Covers (MSC) approach to optimize sensor energy utilization. The paper introduces the MSC problem, proves its NP-Complete nature, and presents two heuristics to tackle it. The paper outlines an Integer Programming model and a Greedy Heuristic to efficiently solve the MSC problem and maximize target coverage while minimizing energy consumption. By scheduling sensor node activities to alternate between sleep and active modes, the proposed approach offers a practical solution to enhance energy efficiency in wireless sensor networks.

ellisd
Download Presentation

Energy-Efficient Target Coverage in Wireless Sensor Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Energy-Efficient Target Coverage in Wireless Sensor Networks PLLAB 김성민

  2. Outline • Introduction • Proposal • Maximum Set Covers(MSC) Problem • MSC Problem is NP-Complete • MSC heuristic • Conclusion

  3. Introduction • Characteristics of WSN • Dense • Limited resourse • … Critical Issue: Power Scarcity!!!

  4. Target Coverage Problem • Given • m targets • n sensors randomly deployed • Assume • same remaining energy • same range • How to optimize the sensor energy utilization?

  5. Proposal C = {s1, s2, s3, s4} R = {r1, r2, r3} Disjoint sets: S1 = {s1, s2} S2 = {s3, s4} Lifetime G = 2 Our Approach: S1 = {s1, s2} with t1 = .5; S2 = {s2, s3} with t2 = .5 S3 = {s1, s3} with t3 = .5; S4 = {s4} with t4 = 1 Lifetime G = 2.5

  6. Proposal

  7. Maximum Set Covers (MSC) • Given C : set of sensors R : set of targets • Goal • Determine a number of set covers S1, …, Sp and t1,…,tp • Where: • Si completely covers R • Maximize t1 + … + tp

  8. Maximum Set Covers (MSC) • Theorem: MSC is NP-Complete • MSC problem belongs to the class NP and is NP-hard, so MSC is NP-Complete • Proof ???? • So, this paper presents Two heuristics.

  9. MSC Heuristic • We first model the MSC problem as an Integer Programming • Given : • A set of n sensor nodes: C = {s1 , s2, …, sn} • A set of m targets: R={r1 , r2, …, rm} • The relationship between sensors and targets: • Ck = { i | sensor si covers target rk} s1 r1 C = {s1, s2, s3}; s2 r2 R = {r1, r2, r3} s3 r3 C1 = {1,3}; C2 = {1,2}; C3 = {2,3} • Variables: • xij = 1 if si ∈ Sj, otherwise xij = 0 • tj ∈ [0, 1], represents the time allocated for Sj

  10. MSC Heuristic (IP) • first constraint : each sensor life time <=1 • second constraint : each target is covered by at least one sensor

  11. MSC Heuristic (IP) • The term xijtj is not linear • Therefore we set yij = xijtj

  12. MSC Heuristic (LP) • We are ready to introduce LP-MSC heuristic

  13. MSC Heuristic (LP) O (p3n3)

  14. Greedy Heuristic • Input parameter • C - the set of sensors • R - the set of targets • w – sensor lifetime granularity, 0 < w <= 1

  15. Greedy Heuristic O (im2n)

  16. Result

  17. Result

  18. Result

  19. Conclusion • Schedule the sensor node activity to alternate between sleep and active mode • Our contributions: • Propose maximum covers set approach • Prove it is NP-complete • Propose an efficient heuristic

  20. 감사합니다

More Related