A Comprehensive Analysis of Metaheuristic based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Underground Wireless Sensor Networks (UWSNs) are sensor networks with sensor nodes placed in lakes, rivers, and other aquatic environments to monitor underwater conditions. Advanced communication protocols, energy harvesting techniques, and improved sensor technologies are essential for overcoming the current limitations. UWSNs face significant challenges due to the dynamic underwater environment, including high signal attenuation and energy constraints. To address these issues, a metaheuristic-based clustering and routing protocol is proposed for UWSNs. Existing models employ metaheuristic optimization, fuzzy logic, and traditional algorithms for clustering and routing in UWSNs. Metaheuristic optimizations such as Clustering-Based Dragonfly Optimization (CDFO), Hierarchical Chimp Optimization (HChO), Chimp Optimization with Hunger Games Search Optimization (ChOA-HGS), Multilayer Clustering-Based Butterfly Optimization (MCBOR), and Enhanced Remora Optimization Algorithm (EROA) have been utilized in the existing models. Additionally, fuzzy logic techniques such as Fuzzy Analytical Hierarchical Process (FAHP), Fuzzy-Based Unequal Clustering Protocol (FBUCP), and traditional algorithms like Grid-Based Protocol, Location-Based Protocol, and Distributed Node Clustering - Mobility Pattern Routing Protocol (DNC-MPRP) have also been deployed for efficient clustering and routing in UWSNs. The clustering using routing protocols for UWSNs are typically evaluated based on metrics such as network lifetime, packet delivery ratio, number of packets received, end-to-end delay, and data transmission efficiency.