Topology Optimization Based on Multi-Objective Genetic Algorithm in Multi-Node Underwater Detection Networks

Underwater detection networks (UDN) are extensively employed in scenarios such as marine environmental monitoring and seabed resource exploration. In general, to achieve higher network coverage efficiency in UDN, nodes need to be deployed as widely as possible, leading to longer distances between nodes. However, the long distances will increase the communication delay between nodes, thus degrading the detection latency performance of UDN. To achieve a balance between the two conflicting performance, i.e., network coverage efficiency and detection latency, this paper proposes a topology optimization scheme based on multi-objective genetic algorithms (MOGA). We utilize multi-objective genetic algorithm to compute the Pareto-optimal topology for each cluster individually. Thus a balance between delay and coverage performance can be achieved. Additionally, the proposed topology strategy is cluster-based to make the network management and practical implement easier. To further improve network performance, we also consider energy consumption as an optimization objective to extend the operational lifetime, thereby enhancing the overall sustainability and reliability of the network. Research results demonstrate that the proposed scheme balances the coverage efficiency and detection latency and achieves a longer network's lifespan.