MeFi: Mean Field Reinforcement Learning for Cooperative Routing in Wireless Sensor Network

Wireless Sensor Networks (WSNs) enable intelligent collaborative perceptions in the Internet of Things. However, devices in WSNs are battery-powered with limited energy resources. During transmission, routing policies significantly affect the energy efficiency in terms of both energy consumption and energy balance among nodes, and further impact the network lifetime. Previous works mostly used heuristic fixed strategies to make routing decisions based on incomplete information in a distributed manner for lower control costs and faster calculation when facing numerous devices in WSNs, which easily lead to performance limitations and routing loops. To this end, we model the network lifetime maximization problem as a Decentralized Partially Observable Markov Decision Process and propose a new scheme MeFi based on Mean Field Reinforcement Learning to perform real-time energy-efficient routing policies for WSNs. The utilization of Mean Field Theory effectively simplifies the intractable interactions among numerous agents and guides the policy training. Additionally, a prioritized-sampling loop-free algorithm is developed to eliminate routing loops and avoid routing policies with significant energy consumption. Experimental results show that our scheme outperforms several algorithms by up to 50%, significantly enhancing energy efficiency and extending WSN lifetime under different circumstances.