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Approximation Algorithm for Maximum Lifetime in Wireless Sensor Networks with Data Aggregation

机译:具有数据聚合的无线传感器网络中最大寿命的近似算法

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We consider the problem of maximizing the lifetime of sensor networks with data aggregation, which was previously investigated by Kalpakis et al. [1]. They propose an exact polynomial-time algorithm, which however is very slow -O n15 logn , and faster heuristics. In this paper, we demonstrate an alternative approach based on the Garg-K ¨onemann algorithm [2] for packing linear programs, combined with the exact computation of minimum cost arborescence. For any varepsilon gt 0, our approach obtains a solution achieving at least 1 - varepsilon times the optimum lifetime, with running time o(n^3 frac{1} {varepsilon }log 1 + varepsilon ^n ). The simulation result shows that our algorithm achieves a solution that is within 2.5% of optimum and is not slower than the heuristics previously proposed by Kalpakis et al.
机译:我们考虑最大化传感器网络的使用数据聚集的问题,以前由Kalpakis等人调查。 [1]。他们提出了一种精确的多项式 - 时间算法,但是非常慢的-O n15 logn,并且更快的启发式。在本文中,我们证明了一种基于GARG-k¨emann算法[2]的替代方法,用于包装线性程序,结合最小成本凝固的精确计算。对于任何varepsilon gt 0,我们的方法获得了实现最佳寿命至少1 - 杂志次数的解决方案,运行时间O(n ^ 3 frac {1} {varepsilon} log 1 + varepsilon ^ n)。仿真结果表明,我们的算法达到了最佳的2.5%内的解决方案,而不是Kalpakis等人提出的启发式速度慢。

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