Abstract
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.
Highlights
A key challenge that limits the applications of wireless sensor networks (WSNs) is the battery lifetime generated by the restricted energy storage of each sensor node
It is obvious that the total number of data packets transmitted by the network increases as the network is trained by the cooperative reinforcement learning (CRL), and the data oscillate around 170,000 packets to 218,000 packets
The adaptive power management system of this article uses cooperative Q-learning and SARSA(l) algorithm to optimize the data transmission rate of EH-WSNs, rather than relying on the manual configuration of the node transmission rate. This is the first application of CRL to adaptively manage the transmission rate of nodes in clustered solar-powered WSNs
Summary
A key challenge that limits the applications of wireless sensor networks (WSNs) is the battery lifetime generated by the restricted energy storage of each sensor node. Data collected by cluster members (CMs) are transmitted to their cluster heads (CHs) and received by the base station (BS) The goal of this network is to maintain an energy neutrality state and maximize the effective data collection of the BS. A WSN needs to dynamically meet application-level QoS requirements, that is, to adjust its own operating state according to changes in the deployed environment. The agents share their CH energy information cooperatively within clusters. An adaptive power manager in EH-WSNs by CRL methods is proposed to increase the network throughput by sharing the information between sensor nodes in one cluster, such as residual battery energy and harvesting energy. The experimental results and discussion are presented in Section ‘‘Simulation and results.’’ Section ‘‘Conclusion’’ is the conclusion of this article
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More From: International Journal of Distributed Sensor Networks
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