Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

Reinforcement Learning (RL) has emerged as a promising approach for improving the performance of Wireless Sensor Networks (WSNs). The Q-learning technique is one RL in which algorithm continuously learns by interacting with environment, gathering information to take certain actions. It maximizes determining optimal result from that environment. In this paper, we propose data based on named Bounded Hop Count - Algorithm (BHC-RLA). proposed uses reward function select set Cluster Heads (CHs) balance between energy-saving and data-gathering latency mobile Base Station (BS). particular, selects groups CHs receive sensing cluster nodes within bounded hop count forward BS when it arrives. addition, are selected minimize tour length. Extensive experiments simulation were conducted evaluate against another traditional heuristic algorithm. We demonstrate outperforms existing work mean length network’s lifetime.