Complexity of graph partition problems
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
On k-connectivity for a geometric random graph
Random Structures & Algorithms
Geography-informed energy conservation for Ad Hoc routing
Proceedings of the 7th annual international conference on Mobile computing and networking
Proceedings of the 7th annual international conference on Mobile computing and networking
Approximation Algorithms for Connected Dominating Sets
ESA '96 Proceedings of the Fourth Annual European Symposium on Algorithms
Routing in Ad Hoc Networks Using a Spine
IC3N '97 Proceedings of the 6th International Conference on Computer Communications and Networks
Range-free localization schemes for large scale sensor networks
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Latency of wireless sensor networks with uncoordinated power saving mechanisms
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A clique base node scheduling method for wireless sensor networks
Journal of Network and Computer Applications
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Wireless Personal Communications: An International Journal
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This paper presents a Connectivity based Partition Approach (CPA) to reduce the energy consumption of a sensor network by sleep scheduling among sensor nodes. CPA partitions sensors into groups such that a connected backbone network can be maintained by keeping only one arbitrary node from each group in active status while putting others to sleep. Nodes within each group swap between active and sleeping status occasionally to balance the energy consumption. Unlike previous approaches that partition nodes geographically, CPA is based on the measured connectivity between pairwise nodes and does not depend on nodes' locations. In this paper, we formulate node scheduling as a constrained optimal graph partition problem, and propose CPA as a distributed heuristic partition algorithm. CPA can ensure k-vertex connectivity of the backbone network for its partition so as to achieve the trade-off between saving energy and preserving network communication quality. Moreover, simulation results show that CPA outperforms other approaches in complex environments where the ideal radio propagation model does not hold.