Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
A New Parallel and Distributed Shortest Path Algorithm for Hierarchically Clustered Data Networks
IEEE Transactions on Parallel and Distributed Systems
Exposure in wireless Ad-Hoc sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
Computational Geometry in C
The coverage problem in a wireless sensor network
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
On k-coverage in a mostly sleeping sensor network
Proceedings of the 10th annual international conference on Mobile computing and networking
Worst and Best-Case Coverage in Sensor Networks
IEEE Transactions on Mobile Computing
Integrated coverage and connectivity configuration for energy conservation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Coverage by randomly deployed wireless sensor networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
On k-Nearest Neighbor Voronoi Diagrams in the Plane
IEEE Transactions on Computers
Optimal k-support coverage paths in wireless sensor networks
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Coverage in wireless ad hoc sensor networks
IEEE Transactions on Computers
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Coverage quality is one critical metric to evaluate the Quality of Service (QoS) provided by wireless sensor networks. In this paper, we address maximum support coverage problem (a.k.a. best case coverage) in wireless sensor networks. Most of the existing work assume that the coverage degree is 1, i. e. every point on the resultant path should fall within the sensing range of at least one sensor node. Here we study the k-coverage problem, in which every point on the resultant path is covered by at least k sensors while optimizing certain objectives. We present tackle this problem under both centralized and distributed setting. The time complexity is bounded by O(k2n log n) where n is the number of deployed sensor nodes. To the best of our knowledge, this is the first work that presents polynomial time algorithms that find optimal k-support paths for a general k.