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
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
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Application-specific protocol architectures for wireless networks
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Proceedings of the 9th annual international conference on Mobile computing and networking
Rate allocation in wireless sensor networks with network lifetime requirement
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
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
Capacity bounds for three classes of wireless networks: asymmetric, cluster, and hybrid
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Minimizing energy consumption in wireless sensor networks using multi-hop relay stations
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
Computers and Operations Research
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Base station placement has significant impact on sensor network performance. Despite its significance, results on this problem remain limited, particularly theoretical results that can provide performance guarantee. This paper proposes a set of procedure to design (1 - ɛ) approximation algorithms for base station placement problems under any desired small error bound ɛ 0. It offers a general framework to transform infinite search space to a finite-element search space with performance guarantee. We apply this procedure to solve two practical problems. In the first problem where the objective is to maximize network lifetime, an approximation algorithm designed through this procedure offers 1/ɛ2 complexity reduction when compared to a state-of-the-art algorithm. This represents the best known result to this problem. In the second problem, we apply the design procedure to address base station placement problem when the optimization objective is to maximize network capacity. Our (1 - ɛ) approximation algorithm is the first theoretical result on this problem.