STOCHASTIC DYNAMIC MODEL OF UNDERWATER WIRELESS SENSOR NETWORK BASED ON LOUVAIN CLUSTERING ALGORITHM
Abstract
Underwater wireless sensor networks (UWSNs) play an important role in monitoring ocean processes, underwater navigation, environmental control and security. However, underwater environment features such as high signal attenuation, limited energy resources and changing network topology create significant challenges in organizing efficient data transmission. To optimize network operation and extend its service life, a clustering method is used to group nodes, reduce the load on communication channels and improve energy efficiency. However, in the event of network node failure, static clustering becomes ineffective, which requires the implementation of dynamic reclustering. The procedure of redistributing node roles and rebuilding the network topology allows maintaining communication stability and minimizing data losses, taking into account the energy balance of the entire network as a whole. This paper examines modern approaches to clustering and reclustering in UWSNs taking into account the energy balance, node failure probability and interference in the transmission medium. The development of adaptive UWSN control methods is an urgent task aimed at increasing the reliability, energy efficiency and durability of underwater communication networks. The article presents a stochastic cross-level model for dynamic three-dimensional PBSNs of arbitrary topology. The model uses a new clustering/reclustering technique based on the Louvain algorithm, a routing protocol built on the Dijkstra method, and a time-domain management (TDMA) method. The proposed PBSN operating model is the basis for the developed simulation complex, which allows assessing the efficiency and reliability of the network, taking into account the loss of connectivity and vulnerabilities for PBSNs of various scales and purposes. As part of the research, a parametric analysis of systematic calculations of the PBSN functional characteristics was performed. The results of the analysis showed that the proposed simulation model provides an increase in the autonomous network operation time and a decrease in the number of lost messages compared to the models of other authors
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