Stochastic Dynamic Model of an Underwater Wireless Sensor Network Based on a Modified Louvain Clustering Algorithm

This paper presents an innovative approach to clustering and routing in Underwater Wireless Sensor Networks (UWSNs), based on a modified Louvain algorithm that considers sensor distances, the probability of successful message delivery, and the current energy levels of the nodes. The proposed method incorporates a dynamic reclustering mechanism driven by real-time monitoring of energy resources, allowing the network to adapt to environmental changes and sensor status by redistributing roles and restructuring clusters accordingly. The developed algorithm is designed to enhance energy efficiency, minimize data loss, and reduce the number of retransmissions in the context of limited bandwidth in underwater acoustic communication channels. A TDMA-based MAC protocol is also implemented to prevent collisions by assigning independent time slots to sensors, thereby eliminating interference. The approach addresses key resource management challenges in UWSNs by reducing energy consumption, improving data delivery reliability, shortening overall message transmission time, and extending the network’s autonomous operation. The model takes into account the three-dimensional spatial deployment of sensors and optimizes the placement of reference nodes to avoid bottlenecks and excessive energy drain. The primary goal of the study is to construct a network topology that minimizes energy costs and message loss while ensuring efficient routing of data to reference nodes and onward to a mobile sink. The flexibility and adaptability of the proposed solution make it well-suited for real-world underwater applications such as environmental monitoring and ocean exploration.