Power management in wireless sensor networks is very important due to the limited energy of batteries. Sensor nodes with harvesters can extract energy from environmental sources as supplemental energy to break this limitation. In a clustered solar-powered sensor network where nodes in the network are grouped into clusters, data collected by cluster members are sent to their cluster head and finally transmitted to the base station. The goal of the whole network is to maintain an energy neutrality state and to maximize the effective data throughput of the network. This article proposes an adaptive power manager based on cooperative reinforcement learning methods for the solar-powered wireless sensor networks to keep harvested energy more balanced among the whole clustered network. The cooperative strategy of Q-learning and SARSA(λ) is applied in this multi-agent environment based on the node residual energy, the predicted harvested energy for the next time slot, and cluster head energy information. The node takes action accordingly to adjust its operating duty cycle. Experiments show that cooperative reinforcement learning methods can achieve the overall goal of maximizing network throughput and cooperative approaches outperform tuned static and non-cooperative approaches in clustered wireless sensor network applications. Experiments also show that the approach is effective in response to changes in the environment, changes in its parameters, and application-level quality of service requirements.

由于电池的能量有限，无线传感器网络中的电源管理非常重要。带有收割机的传感器节点可以从环境资源中提取能量作为补充能量，以打破此限制。在群集的太阳能传感器网络中，将网络中的节点分为群集，群集成员收集的数据将发送到其群集头，最后传输到基站。整个网络的目标是保持能量中立状态并最大化网络的有效数据吞吐量。本文提出了一种基于协作式强化学习方法的自适应电源管理器，用于太阳能无线传感器网络，以使整个群集网络中的能量收集更加均衡。Q的合作策略-学习和SARSA（λ）基于节点剩余能量，下一个时隙的预测收获能量以及簇头能量信息被应用到此多主体环境中。节点相应地采取行动以调整其工作占空比。实验表明，协作强化学习方法可以达到最大化网络吞吐量的总体目标，在集群无线传感器网络应用中，协作方法的性能要优于静态和非协作方法。实验还表明，该方法可有效响应环境的变化，其参数的变化以及应用程序级别的服务质量要求。