Fuzzy Based Energy Efficient Routing for IoT: Traffic Delay Optimization

Energy conservation and optimized traffic delay are crucial challenges in Internet of Things (IoT) systems, particularly in wireless sensor networks (WSNs). This study presents a novel approach to address these issues through a fuzzy-based routing protocol. Traditional methods often struggle to effectively enhance routing protocols with optimized packet size control. Researchers have proposed a combination of machine learning and evolutionary techniques to overcome this limitation and enhance energy efficiency in WSNs. The new approach called MOSPFNN (Multi-Objective Spider Prey-localized Fuzzy Neural Network) leverages fuzzy logic control (FLC) to select optimal paths for traffic-aware multipath routing. Additionally, a new meta-heuristic algorithm of SP optimizer is employed to develop an energy-efficient spatial routing protocol with superior convergence and minimal local optima. The proposed protocol incorporates optimal FNN for congestion monitoring and traffic-aware routing. Simulation results validate that new protocol outperforms existing methods in terms of average end-to-end delay and packet delivery ratio (PDR). Using MOSPFNN, traffic with different priority levels can achieve a successful PDR rate of over 92% and network lifetime of above 2% than previous network within a reasonable timeframe. This research contributes to advancing energy-efficient and traffic delay-aware routing protocols in IoT systems.