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
Achieving Real-Time Target Tracking UsingWireless Sensor Networks
RTAS '06 Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium
VigilNet: An integrated sensor network system for energy-efficient surveillance
ACM Transactions on Sensor Networks (TOSN)
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
Coverage breach problems in bandwidth-constrained sensor networks
ACM Transactions on Sensor Networks (TOSN)
Selected Topics in Column Generation
Operations Research
SensorMap for Wide-Area Sensor Webs
Computer
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 is an important yet challenging problem in wireless sensor networks, especially when both coverage and energy constraints should be taken into account. Due to its nonlinear nature, previous studies of this problem have mainly focused on heuristic algorithms; the theoretical bound remains unknown. Moreover, the most popular method used in the previous literature, i.e., discretization of continuous time, has yet to be justified. This paper fills in these gaps with two theoretical results. The first one is a formal justification for the method. We use a simple example to illustrate the procedure of transforming a solution in time domain into a corresponding solution in the pattern domain with the same network lifetime and obtain two key observations. After that, we formally prove these two observations and use them as the basis to justify the method. The second result is an algorithm that can guarantee the network lifetime to be at least (1 - ε) of the optimal network lifetime, where ε can be made arbitrarily small depending on the required precision. The algorithm is based on the column generation (CG) theory, which decomposes the original problem into two sub-problems and iteratively solves them in a way that approaches the optimal solution. Moreover, we developed several constructive approaches to further optimize the algorithm. Numerical results verify the efficiency of our CG-based algorithm.