An OL(n3) potential reduction algorithm for linear programming
Mathematical Programming: Series A and B
Solving binary cutting stock problems by column generation and branch-and-bound
Computational Optimization and Applications
Maximal lifetime scheduling for K to 1 sensor-target surveillance networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Maximal Lifetime Scheduling for Sensor Surveillance Systems with K Sensors to One Target
IEEE Transactions on Parallel and Distributed Systems
Selected Topics in Column Generation
Operations Research
Lifetime maximization for connected target coverage in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Energy-efficient coverage problems in wireless ad-hoc sensor networks
Computer Communications
QoS-aware target coverage in wireless sensor networks
Wireless Communications & Mobile Computing
Energy-Efficient connected coverage of discrete targets in wireless sensor networks
ICCNMC'05 Proceedings of the Third international conference on Networking and Mobile Computing
IEEE Communications Magazine
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The target coverage problem is one of the most fundamental challenges in wireless sensor networks. Due to the complexity of the problem (time-dependent network topology and coverage constraints), previous studies have mainly focused on heuristic algorithms and the theoretical bound remains unknown. In this paper, we aim to fill in this gap by providing fundamental results. First, we investigate the properties of a problem in time domain via an example topology and build a novel transformation to connect a problem in the time domain with a corresponding problem in the space domain while maintaining the same network lifetime. Based on this transformation, we mathematically formulate the problem and build a column-generation based algorithm, which decomposes the original formulation into two sub-formulations and iteratively solves them in a way that approaches the optimal solution. We prove that the network lifetime that can be guaranteed by the proposed algorithm is at least (1-ε) of the optimum, where ε can be made arbitrarily small depending on the required precision.