Optimizing Underwater IoT Routing with Multi-Criteria Decision Making and Uncertainty Weights (2405.11513v1)

Abstract: Effective data routing is vital in the Internet of Things (IoT) paradigm, especially in underwater mobile sensor networks where inefficiency can lead to significant resource consumption. This article presents an innovative method designed to enhance network performance and reduce resource usage, while also accurately determining component weights in these networks, ensuring quality service. Building upon previous research on multi-criteria decision-making systems in coastal RPL networks, our method involves key adaptations for underwater environments. It integrates comprehensive network features to identify the optimal parent node for each sensor, employing the fuzzy SWARA decision-making approach under uncertain conditions. This method takes into account various factors including hops, energy, ARSSI rate, delay, ETX, link delivery rate, and depth to determine the most effective parent node assignment. Through simulation, our approach demonstrates marked improvements in network performance compared to existing solutions. These advancements are significant, offering a new direction in enhancing underwater IoT communications and suggesting wider applications for IoT systems facing similar challenges.