Fuzzy Routing in Communication Network Optimization: An Adaptive Algorithm for the Internet of Things (IoT)

Abstract The increasing complexity of Internet of Things (IoT) networks presents significant challenges in optimizing communication and routing efficiency. Traditional routing protocols struggle to adapt to dynamic topologies, energy constraints, and varying data transmission needs. Fuzzy routing has emerged as a promising solution, utilizing fuzzy logic to enhance decision-making under uncertainty. Simulation results in recent studies suggest that fuzzy routing methods outperforms conventional protocols reducing energy consumption while extending network lifetime. Adaptive routing enhances the reliability of data transmission as well as minimizes congestion with only a minor increment in end-to-end delay due to hierarchical routing. The scenarios seem to provide examples of how fuzzy logic can devise IoT networks to be more resilient and scalable. Future research must be directed toward the promising area of merging machine learning with fuzzy routing, leading to self-optimizing and predictive algorithms. Such advancement would improve the real-time adaptability, as well as work toward reducing the latency and increasing the efficiency and sustainability of IoT communications.