Intelligent Networking for Energy Harvesting Powered IoT Systems

As the next-generation battery substitute for IoT system, energy harvesting (EH) technology revolutionize the IoT industry with environmental friendliness, ubiquitous accessibility, and sustainability, which enables various self-sustaining IoT applications. However, due to the weak and intermittent nature of EH power, the performance of EH-powered IoT systems as well as its collaborative routing mechanism can be severely deteriorated rendering unpleasant data package loss during each power failure. Such a phenomenon makes conventional routing policies and energy allocation strategies impractical. Given the complexity of the problem, reinforcement learning (RL) appears to be one of the most promising and applicable methods to address this challenge. Nevertheless, even that the energy allocation and routing policy are jointly optimized by the RL method, due to the energy restriction of EH devices, the inappropriate configuration of multi-hop network topology severely degrades the data collection performance. Therefore, this paper first conducts a thorough mathematical discussion and develops the topology design and validation algorithm under energy harvesting scenarios. Then, this paper develops DeepIoTRouting, a distributed and scalable deep reinforcement learning (DRL) - based approach, to address the routing and energy allocation jointly for the energy harvesting powered distributed IoT system. The experimental results show that with topology optimization, DeepIoTRouting achieves at least \(38.71\% \) improvement on the amount of data delivery to sink in a 20-device IoT network, which significantly outperforms state-of-the-art methods.