A coverage-preserving node scheduling scheme for large wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Set k-cover algorithms for energy efficient monitoring in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Integrated coverage and connectivity configuration for energy conservation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
DEEPS: Deterministic Energy-Efficient Protocol for Sensor networks
SNPD-SAWN '06 Proceedings of the Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Maximum Lifetime of Sensor Networks with Adjustable Sensing Range
SNPD-SAWN '06 Proceedings of the Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Advances in parallel and distributed computing models - APDCM
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
HiPC'07 Proceedings of the 14th international conference on High performance computing
IEEE Communications Magazine
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A major challenge in Wireless Sensor Networks is that ofmaximizing the lifetime while maintaining coverage of a set of targets,a known NP-complete problem. In this paper, we present theoretically-grounded, energy-efficient, distributed algorithms that enable sensors toschedule themselves into sleep-sense cycles. We had earlier introduceda lifetime dependency (LD) graph model that captures the interdependenciesbetween these cover sets by modeling each cover as a node andhaving the edges represent shared sensors. The key motivation behindour approach in this paper has been to start with the question of whatan optimal schedule would do with the lifetime dependency graph. Weprove some basic properties of the optimal schedule that relate to theLD graph. Based on these properties, we have designed algorithms whichchoose the covers that exhibit these optimal schedule like properties. Wepresent three new sophisticated algorithms to prioritize covers in thedependency graph and simulate their performance against state-of-art algorithms. The net effect of the 1-hop version of these three algorithmsis a lifetime improvement of more than 25-30% over the competing algorithmsof other groups, and 10-15% over our own; the 2-hop versionshave additional improvements, 30-35% and 20-25%, respectively.