Neuro Fuzzy Deep Reinforcement Learning for Energy Optimized Routing in Large-Scale Wireless Sensor Networks

Abstract Wireless Large scale Wireless Sensor Networks (WSN), composed of numerous sensors pivots the significant applications like smart city, environmental monitoring, industrial automation etc. The energy efficient routing protocols of the normal WSN experiences a major setback in terms of scalability, energy efficiency, routing overhead, data transmission, and fault tolerance. The increase in number of nodes significantly enhances the communication overhead and energy consumption. Moreover, the large scale WSN relies on the multi-hop communication for the data transfer from distant node to the sink node, creating communication bottlenecks. To overcome these concerns, the proposed work introduces a novel scalable Neuro-Fuzzy Deep Reinforcement learning algorithm for the energy efficient routing process. This algorithm integrates the capabilities of the neuro-fuzzy systems with the decision making capability of the Deep Reinforcement Learning (DRL) model to optimize the energy consumption and to ensure the prolonged network lifespan. The neuro-fuzzy component in the proposed work leverages the fuzzy logic to handle the uncertainties in the large scale WSN, such as dynamic topology, and the variable link quality while the neural network enhances the learning adaptability. The proposed work is analyzed in terms of energy efficiency, network lifetime, packet delivery ratio, throughput, end-to-end delay, scalability, and routing overhead to compare with the existing state of the art routing methodologies.