Exposure in wireless Ad-Hoc sensor networks
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WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
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FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
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Maximum lifetime routing in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
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LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
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IEEE/ACM Transactions on Networking (TON)
Maximal lifetime scheduling for K to 1 sensor-target surveillance networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Energy-efficient coverage problems in wireless ad-hoc sensor networks
Computer Communications
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 problems in sensor networks: A survey
ACM Computing Surveys (CSUR)
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International Journal of Sensor Networks
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In this paper, we consider the problem of scheduling sensor activities to maximize network lifetime while maintaining both discrete K-target coverage and network connectivity. In K-target coverage, it is required that each target should be simultaneously observed by at least K sensors. The data generated by the sensors will be transmitted to the sink node via single or multiple hop communications. As maintaining discrete target coverage cannot guarantee the network connectivity, we consider both target coverage and connectivity issues. Further, by adopting a more realistic energy consumption model, we consider the sensor activity scheduling problem and routing problem jointly. We study the problem with two observation scenarios depending on whether a sensor can distinguish the targets in its sensing area or not. For the first scenario, a more general scenario where each sensor can simultaneously observe multiple targets is considered and we develop a polynomial-time algorithm which can achieve optimal solution based on linear programming and integer theorem. For the second scenario, we show that the problem is NP-complete and develop an approximation algorithm for solving it. As the protocol cost of the optimal solution and the approximation algorithm may be high in practice, we develop a low-cost heuristic algorithm which can be implemented in a distributed fashion for both scenarios. We demonstrate the effectiveness of the heuristic algorithm through extensive simulations.