Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks
IEEE Transactions on Computers
Online Multicasting for Network Capacity Maximization in Energy-Constrained Ad Hoc Networks
IEEE Transactions on Mobile Computing
Energy-efficient coverage for target detection in wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Randomized Approach for Target Coverage Scheduling in Directional Sensor Network
ICESS '07 Proceedings of the 3rd international conference on Embedded Software and Systems
Energy-Efficient connected coverage of discrete targets in wireless sensor networks
ICCNMC'05 Proceedings of the Third international conference on Networking and Mobile Computing
Coverage for target localization in wireless sensor networks
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
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Target coverage is a fundamental problem in sensor networks for environment monitoring and surveillance purposes. To prolong the network lifetime, a typical approach is to partition the sensors in a network for target monitoring into several disjoint subsets such that each subset can cover all the targets. Thus, each time only the sensors in one of such subsets are activated. It recently has been shown that the network lifetime can be further extended through the overlapping among these subsets. Unlike most of the existing work in which either the subsets were disjoint or the sensors in a subset were disconnected, in this paper we consider both target coverage and sensor connectivity by partitioning an entire lifetime of a sensor into several equal intervals and allowing the sensor to be contained by several subsets to maximize the network lifetime. We first analyze the energy consumption of sensors in a Steiner tree rooted at the base station and spanning the sensors in a subset. We then propose a novel heuristic algorithm for the target coverage problem, which takes into account both residual energy and coverage ability of sensors. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm by varying the number of intervals of sensor lifetime and network connectivity. The experimental results show that the network lifetime delivered by the proposed algorithm is further prolonged with the increase of the number of intervals and improvement of network connectivity.