学习自动机结合节点功率自适应调整的WSN目标覆盖方案

发布时间：2018-05-05 16:30

本文选题：无线传感器网络(WSN) + 节点功率　；参考：《计算机应用研究》2017年01期

【摘要】：针对大多数现有无线传感器网络目标覆盖方案没有考虑传感器功率(传感范围)可调的问题,提出一种基于学习自动机(learning automata,LA)和节点功率自适应调整的WSN的目标覆盖方案。利用LA算法根据节点能量自适应调整节点的发射功率,构建能够覆盖所有目标的覆盖集,并通过精简过程获得最小覆盖集,从而减低节点的能耗,提高网络的生命周期。通过实验研究了传感器数量和目标数量对网络寿命的影响,并将该方案与基于贪婪算法、遗传算法的方案进行比较,结果表明,该方案能够获得更多的覆盖集和更长的网络寿命。
[Abstract]:In view of the fact that most existing target coverage schemes in wireless sensor networks do not consider the adjustable sensor power (sensor range), a target coverage scheme based on learning automat la and adaptive adjustment of node power for WSN is proposed. The LA algorithm adaptively adjusts the transmit power of the node according to the node energy, constructs the covering set which can cover all the targets, and obtains the minimum cover set by reducing the process, thus reducing the energy consumption of the node and increasing the lifetime of the network. The effects of the number of sensors and the number of targets on the network life are studied through experiments. The results show that the proposed scheme is compared with the scheme based on greedy algorithm and genetic algorithm. This scheme can obtain more overlay sets and longer network lifetime.
【作者单位】：中原工学院计算机学院;武汉大学计算机学院;
【基金】：国家自然科学基金资助项目(61373169) 国家“863”计划资助项目(2013AA122301)
【分类号】：TP212.9;TN929.5

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