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Graph Cellular Automata approach to the Maximum Lifetime Coverage Problem in wireless sensor networks

机译:图元自动机方法解决无线传感器网络中的最大使用寿命

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In this paper, we propose a distributed algorithm based on a generalization of the Cellular Automata concept called Graph Cellular Automata (GCA) to solve the Maximum Lifetime Coverage Problem (MLCP) in wireless sensor networks (WSNs). In GCA, we adapt life-like state transition functions inspired by Conway's Game of Life in order to solve the problem. The goal of this paper is to study the quality of state transition functions for an objective provided by the MLCP in WSNs. The proposed algorithm possesses all the advantages of a localized algorithm, i.e., using only some knowledge about neighbors, a WSN is able to self-organize in such a way as to prolong its lifetime, at the same time preserving the required coverage ratio of the target field. Our experimental results show that certain rules are better solvers of the given problem than others. The paper also presents the results of an experimental study of the proposed algorithm and comparison with a centralized Genetic Algorithm.
机译:在本文中,我们提出了一种基于蜂窝自动机概念的通用算法,称为图蜂窝自动机(GCA),以解决无线传感器网络(WSN)中的最大寿命覆盖问题(MLCP)。在GCA中,我们改编自Conway的《生命游戏》启发的逼真的状态转换功能,以解决该问题。本文的目的是研究WSN中MLCP提供的目标状态转换函数的质量。提出的算法具有局域化算法的所有优点,即仅使用有关邻居的一些知识,WSN就能以延长其寿命的方式自组织,同时保留所需的覆盖范围。目标字段。我们的实验结果表明,某些规则比其他规则更好地解决了给定的问题。本文还介绍了该算法的实验研究结果,并与集中式遗传算法进行了比较。

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