Efficient point coverage in wireless sensor networks

Jie Wang () and Ning Zhong ()
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Jie Wang: University of Massachusetts
Ning Zhong: University of Massachusetts

Journal of Combinatorial Optimization, 2006, vol. 11, issue 3, No 3, 304 pages

Abstract: Abstract We study minimum-cost sensor placement on a bounded 3D sensing field, R, which comprises a number of discrete points that may or may not be grid points. Suppose we have ℓ types of sensors available with different sensing ranges and different costs. We want to find, given an integer σ ≥ 1, a selection of sensors and a subset of points to place these sensors such that every point in R is covered by at least σ sensors and the total cost of the sensors is minimum. This problem is known to be NP-hard. Let k i denote the maximum number of points that can be covered by a sensor of the ith type. We present in this paper a polynomial-time approximation algorithm for this problem with a proven approximation ratio $${\gamma = \sum_{i=1}^{\ell} k_{i} -\sigma+1}$$ . In applications where the distance of any two points has a fixed positive lower bound, each k i is a constant, and so we have a polynomial-time approximation algorithms with a constant guarantee. While γ may be large, we note that it is only a worst-case upper bound. In practice the actual approximation ratio is small, even on randomly generated points that do not have a fixed positive minimum distance between them. We provide a number of numerical results for comparing approximation solutions and optimal solutions, and show that the actual approximation ratios in these examples are all less than 3, even though γ is substantially larger.

Keywords: Sensor placement; Approximation algorithms; Linear programming (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10878-006-7909-z

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