Intelligent data routing strategy based on federated deep reinforcement learning for IOT-enabled wireless sensor networks

Abstract

The high-speed data routing in the Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs) is vital in improving network performance. Existing routing protocols face difficulties dealing with frequent node relocations, energy efficiency optimization, scalability, and dynamic network environments. The inadequacy of conventional routing algorithms in handling the issues provided by frequent node relocations, energy efficiency optimization, scalability, and dynamic network settings in IoT-enabled WSNs is identified as the problem. Traditional protocols are inflexible in the face of node relocations, whereas machine learning-based techniques frequently miss energy efficiency factors. Reinforcement learning-based techniques presume static topologies and the potential of federated learning in WSN routing is largely untapped. The goal is to provide adaptive and energy-efficient routing while considering message overhead, temporal complexity, data sum rate, communication delay, and scalability. The research adopts federated deep reinforcement learning (FDRL) to address these limitations for routing the high-speed data packets in IoT-enabled WSNs. The proposed research technique uses FDRL, which enables distributed decision-making and adaptive routing in dynamic network situations. The fundamental instant data load is separated into cluster pairs in the proposed technique, each with one strong and one weak sensor node. This split enables optimal network load balancing and resource use. The FDRL architecture is used to disperse the learning process across numerous nodes or access points, allowing for localized decision-making while taking advantage of global coordination. The simulations are conducted to validate the effectiveness of the FDRL Routing in terms of packet loss, latency, energy efficiency, and scalability. The results show that the proposed federated DRL-based intelligent data routing approach outperforms traditional protocols and other current routing strategies.