Hybrid Deep Reinforcement Learning and Bio-Inspired Optimization for Adaptive Routing and Clustering in Wireless Sensor Networks

Abstract

Wireless Sensor Networks WSNs are widely adopted and cost-effective way of implementing intelligent solutions in many low-resource settings. Both known Clustering/Routing Protocols such as Low-Energy Adaptive Clustering Hierarchy LEACH and Threshold-Sensitive Energy-Efficient Network Protocol TEEN as well as other related methods such as WOA-LEACH, are severely limited by their inability to support a long term network cycle for use in real world applications. This is because they all experience rapid energy exhaustion because of their inability to adapt to varying levels of energy and traffic demand. In addition, failure to distribute energy usage evenly throughout the network. In order to resolve these issues, we will develop a new Hybrid Approach that combines the Whale Optimization Algorithm WOA for determining energy aware cluster heads, and Deep Reinforcement Learning DRL for providing an adaptive multi-hop routing protocol. The proposed hybrid DRL-WOA solution will make joint optimizations of cluster heads and routing nodes to determine optimized routes to minimize energy use while maximizing energy efficiency through the optimization of hop distances, thereby creating longer lasting and more reliable communication processes. Results from simulations run on a 100 node WSN environment demonstrate the hybrid DRL-WOA solution achieves better performance than LEACH, TEEN, WOA-LEACH and a DQN-based only routing solution, including 22% less total energy consumption, 60% extended First Node Death FND, and PDR improvements of 5-15% in comparison to each of the above mentioned base line protocols. All in all, the experimental results clearly demonstrate that the proposed Hybrid DRL-WOA approach leads to a considerable improvement of the energy efficiency, network lifetime and the reliability of data delivery of static WSNs.