Theoretical Framework for Efficient UV-Relay Selection in IoUT Using Hybrid NN-GA Model with MCDM Approach

Routing protocols are one of the most crucial strategies for ensuring successful network operations on the Internet of Underwater Things (IoUT). By ensuring optimal data transmission in difficult underwater environments, clustering energy-efficient routing protocols based on relay assistants can extend the network lifetime. A theoretical modeling for selecting underwater vehicles (UV s) to serve as relays between cluster heads (CHs) and surface base stations (SBS) is presented in this paper. The proposed model is based on a hierarchical hybrid neural network (NN) and genetic algorithms (GA) with the multi-criteria decision making (MCDM) technique. The proposed study's objective is to find the best UVs for IoUT network to optimize routing decisions and energy efficiency. The proposed model performs well in reducing the number of dead UNs and CHs overheads after using the best selected UVs to serve as relay in modified SEP protocol.