在静态自组织网络中最大化可满足的路由请求数量

Zane Sumpter, Lucas Burson, Bin Tang, Xiao Chen
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引用次数: 9

摘要

研究了静态自组织网络中的节能路由问题。这个问题被称为maxR,是在每个节点电池电量有限的约束下,使网络中可以满足的路由请求数量最大化。该问题的在线版本已经得到了广泛的研究,其中必须通过网络路由的消息序列事先是未知的。在本文中,我们研究了该问题的离线版本,其中请求序列是预先知道的。据我们所知,离线maxR问题及其硬度和近似性还没有得到很好的研究。我们证明,经过适当的变换,离线maxR等价于众所周知的np困难的最大不相交路径问题。我们提出了一种贪婪算法,称为GDP,它与最优算法的近似比为常数。GDP可以作为评估在线算法性能的基准,因为我们知道最好的离线算法比任何在线算法的性能都要好。然后,我们提出了一种新的在线算法MECBE来解决在线maxR问题。仿真结果表明,在可满足的请求数量、每个请求的平均能耗和耗尽能量的节点数量方面,GDP优于MECBE, MECBE优于最先进的在线算法OML。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximizing number of satisfiable routing requests in static ad hoc networks
We study an energy-efficient routing problem in static ad hoc networks. The problem, referred to as maxR, is to maximize the number of routing requests that can be satisfied in the network, under the constraint that each node has finite battery power. The online version of the problem, where the sequence of messages that has to be routed over the network is not known ahead of time, has been studied extensively. In this paper, we study the offline version of the problem where the sequence of requests is pre-known. As far as we know, the offline maxR problem, its hardness and approximability have not been well studied. We show that after appropriate transformation, offline maxR is equivalent to the well-known maximum disjoint path problem, which is NP-hard. We propose a greedy algorithm called GDP that has a constant approximation ratio to the optimal algorithm. GDP can be used as a benchmark to evaluate the performance of online algorithms as it is known that the best offline algorithm performs better than any online algorithm. We then put forward a new online algorithm called MECBE to solve the online maxR problem. Simulation results show that GDP outperforms MECBE, which outperforms the state-of-the-art online algorithm OML, in terms of the number of satisfiable requests, the average energy consumption per request, and the number of energy-depleted nodes.
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