Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Adaptive protocols for information dissemination in wireless sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Fault tolerant deployment and topology control in wireless networks
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
FLSS: a fault-tolerant topology control algorithm for wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Optimal monitoring in multi-channel multi-radio wireless mesh networks
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
K-connected target coverage problem in wireless sensor networks
COCOA'07 Proceedings of the 1st international conference on Combinatorial optimization and applications
Algorithms for the m-coverage problem and k-connected m-coverage problem in wireless sensor networks
NPC'07 Proceedings of the 2007 IFIP international conference on Network and parallel computing
Securing wireless mesh networks
IEEE Wireless Communications
A framework for misuse detection in ad hoc Networks-part I
IEEE Journal on Selected Areas in Communications
Studying the stochastic capturing of moving intruders by mobile sensors
Computers & Mathematics with Applications
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In this paper, we study the dominating selection optimisation problem with multiple channels and multiple radios in wireless sensor networks. The objective is to maximise the number of targets covered while selecting at most k nodes and at most ki channels with each selected node vi. Our problem is a general case of the maximum coverage problem. We propose two algorithms: the first one is based on linear programming and PIPAGE rounding, in which its approximation ratio is 1/K(1−(1−1/m)m), where m is the number of the dominating nodes and K = max ki. The second algorithm is based on greedy strategy with low time complexity. The simulation shows that the both two algorithms have good performance.