Approximation algorithms for NP-hard problems
Primal-Dual RNC Approximation Algorithms for Set Cover and Covering Integer Programs
SIAM Journal on Computing
Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks
IEEE Transactions on Computers
Fault-Tolerant Sensor Coverage for Achieving Wanted Coverage Lifetime with Minimum Cost
WASA '07 Proceedings of the International Conference on Wireless Algorithms,Systems and Applications
Minimum-cost sensor arrangement for achieving wanted coverage lifetime
International Journal of Sensor Networks
Coverage problems in sensor networks: A survey
ACM Computing Surveys (CSUR)
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This paper presents a new algorithm for finding better approximation solutions to the min-cost point coverage problem in wireless sensor networks. The problem is to compute a deterministic sensor deployment plan, with minimum monetary cost on sensors, to cover the set of targets spread across a geographical region such that each target is covered by multiple sensors. This is a Max-SNP-complete problem. Our approximation algorithm, called alpha-beta approximation, is a convex combination of greedy LP-rounding and greedy set-cover selection. We show that, through a large number of numerical simulations on randomly generated targets and sites, alpha-beta approximation produces efficiently better approximation results than the best approximation algorithm previously known. In particular, the alpha-beta approximation in our experiments never exceeds an approximation ratio of 1.07, providing up to 14.86% improvement over previous approximation algorithms.