Distributed neuro-fuzzy routing for energy-efficient IoT smart city applications in WSN

Abstract

Wireless sensor networks (WSNs) enable seamless data gathering and communication, facilitating efficient and real-time decision-making in IoT monitoring applications. However, the energy required to maintain communication in WSN-based IoT networks poses significant challenges, such as packet loss, packet drop, and rapid energy depletion. These issues reduce network life and performance, increasing the risk of delayed packet delivery. To address these challenges, this work presents a novel energy-efficient distributed neuro-fuzzy routing model executed in two stages to enhance communication efficiency and energy management in WSN-based IoT applications. In the first stage, nodes with high energy levels are predicted using a fusion of distributed learning with neural networks and fuzzy logic. In the second stage, clustering and routing are performed based on the predicted eligible nodes, incorporating thresholds for energy and distance with two combined metrics. The cluster head (CH) combined metric optimizes cluster head selection, while the next-hop combined metric facilitates efficient multi-hop communication. Extensive simulation results demonstrate that the proposed model significantly enhances network lifetime compared to EANFR, RBFNN T2F, and TTDFP by 9.48%, 25%, and 31.5%, respectively.