Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Maximum flow-life curve for a wireless ad hoc network
MobiHoc '01 Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Application-specific protocol architectures for wireless networks
Application-specific protocol architectures for wireless networks
Topology control for wireless sensor networks
Proceedings of the 9th annual international conference on Mobile computing and networking
On deriving the upper bound of α-lifetime for large sensor networks
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
Enhancing lifetime of wireless sensor networks using multiple data sinks
International Journal of Sensor Networks
A 2-approximation algorithm for optimal deployment of k base stations in WSNs
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part II
Some fundamental results on base station movement problem for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Lifetime aware deployment of k base stations in WSNs
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
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Base station location has a significant impact on network lifetime performance for a sensor network. For a multihop sensor network, this problem is particularly challenging due to its coupling with data routing. This article presents an approximation algorithm that can guarantee (1 − ϵ)-optimal network lifetime performance for base station placement problem with any desired error bound ϵ 0. The proposed (1 − ϵ)-optimal approximation algorithm is based on several novel techniques that makes it possible to reduce an infinite search space to a finite-element search space for base station location. The first technique used in this reduction is to discretize cost parameter (associated with energy consumption) with performance guarantee. Subsequently, the continuous search space can be broken up into a finite number of subareas. The second technique is to exploit the cost property of each subarea and represent it by a novel notion called fictitious cost point, each with guaranteed cost bounds. We give a proof that the proposed base station placement algorithm is (1− ϵ)-optimal. This approximation algorithm is simpler and faster than a state-of-the-art algorithm and represents the best known result to the base station placement problem.