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A distributed monitoring mechanism for wireless sensor networks
WiSE '02 Proceedings of the 1st ACM workshop on Wireless security
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WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
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LCN '03 Proceedings of the 28th Annual IEEE International Conference on Local Computer Networks
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OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Connected K-target coverage problem in wireless sensor networks with different observation scenarios
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GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Coverage problems in sensor networks: A survey
ACM Computing Surveys (CSUR)
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Journal of Computer Science and Technology - Special issue on natural language processing
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Computer Communications
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This paper addresses the maximal lifetime scheduling for k to 1 sensor-target surveillance networks. Given a set of sensors and targets in Euclidean plane, a sensor can watch only one target at a time and a target should be watched by k sensors (k ≥ 2) at anytime. Our task is to schedule sensors to watch targets, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration that all targets are watched. We propose an optimal solution to find the target watching schedule for sensors that achieves the maximal lifetime. This is the first time in the literature that this scheduling problem of sensor surveillance systems has been formulated and the optimal solution has been found. Our solution consists of three steps: (1) computing the maximal lifetime of the surveillance system and a workload matrix by using linear programming techniques; (2) decomposing the workload matrix into a sequence of schedule matrices by extending the Hall's theory, to achieve the maximal lifetime; (3) obtaining a target watching timetable for each sensor based on the schedule matrices. The time complexity of our optimal method is O(m2n3), where m, n are the number of targets and the number of sensors, respectively. We illustrate our optimal method by a numeric example and experiments in the end.