Balanced Energy-Efficient Routing in MANETs using Reinforcement Learning

This paper proposes an energy-efficient path selection algorithm which aims at balancing the contrasting objectives of maximizing network lifetime and minimizing energy consumption routing in mobile ad hoc networks (MANETs). The method is based on a reinforcement learning technique called the on- policy Monte Carlo (ONMC) method. Simulation results show that variants of the proposed method can outperform existing schemes such as variants of the conditional max-min battery capacity routing (CMMBR) and the best minimum combined- cost routing algorithm in terms of the long-term average reward which depicts the balance of the tradeoff in dynamic topology environments.