Energy Harvesting Aware Multi-hop Routing and Energy Allocation in Distributed IoT System based on Multi-agent Reinforcement Learning
TimeTuesday, December 7th6:00pm - 7:00pm PST
LocationLevel 2 - Lobby
Event Type
Networking Reception
Work-in-Progress Poster
Virtual Programs
Presented In-Person
DescriptionEnergy harvesting technologies offer a promising solution to sustainably power an ever-growing number of Internet of Things (IoT) devices. However, due to the weak and transient natures of energy harvesting, IoT devices work intermittently rendering conventional routing policies and energy allocation strategies impractical. Aiming to minimize the data transmission time, this paper developed a distributed multi-agent reinforcement learning algorithm known as independent asynchronous advantage actor-critic (IA3C) to control routing and energy allocation together for the energy harvesting powered IoT system. The experimental results show that the proposed IA3C algorithm can provide outstanding packet delivery rates compared with baseline methods.