Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
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
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Faster and Simpler Algorithms for Multicommodity Flow and other Fractional Packing Problems.
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
On k-coverage in a mostly sleeping sensor network
Proceedings of the 10th annual international conference on Mobile computing and networking
Integrated coverage and connectivity configuration for energy conservation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Energy-efficient coverage problems in wireless ad-hoc sensor networks
Computer Communications
HiPC'07 Proceedings of the 14th international conference on High performance computing
IEEE Communications Magazine
Dynamic topology construction of wireless sensor network using computational geometric approach
International Journal of Sensor Networks
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One of the key challenges in wireless sensor networks (WSNs) is that of extending the lifetime of the network while meeting some coverage requirements. In this paper, we present a distributed algorithmic framework to enable sensors to determine their sleep-sense cycles based on specific coverage goals. The framework is based on our earlier work on the target coverage problem. We give a general version of the framework that can be used to solve network/graph optimisation problems for which melding compatible neighbouring local solutions directly yields globally feasible solutions such as the maximal independent set problem. We also apply this framework to several variations of the coverage problem, namely, target coverage, area coverage and k-coverage problems, to demonstrate its general applicability. Each sensor constructs minimal cover sets for its local coverage objective. The framework entails each sensor prioritising these local cover sets and then negotiating with its neighbours for satisfying mutual constraints. We employ a dependency graph model that can capture the interdependencies among the cover sets. Detailed simulations are carried out to further demonstrate the resulting performance improvements and effectiveness of the framework. These show an improvement of between 10 and 20% over existing algorithms.