学习自动机结合节点功率自适应调整的WSN目标覆盖方案

针对大多数现有无线传感器网络(Wireless Sensor Network, WSN)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。

A Target Coverage Scheme for WSN Based on LA algorithm and Node Power Adaptive Adjustment

For the issues that the most of the existing wireless sensor network(WSN) target coverage scheme without considering the sensor power (sensing range) can be adjusted, a target coverage scheme for wsn based on learning automata(LA) and node power adaptive adjustment is proposed. This scheme uses LA algorithm to adjust the sensing range of nodes according to the energy of nodes, and to build a cover set covering all targets, and the minimum cover set is obtained by minimizing the process, so as to reduce the energy consumption of the nodes and improve the lifetime of the network. Through a number of experiments, the influence of the number of sensors and the number of targets on the network lifetime is studied, and the scheme is compared with the greedy algorithm and genetic algorithm. The results show that the scheme can obtain more cover sets and longer network lifetime.