Intelligent power underwater wireless sensor networks for marine environmental monitoring using a hybrid marine predator–Henry gas solubility optimization approach

Underwater wireless sensor networks (UWSNs) have an important role in ocean monitoring, environmental surveillance and disaster prevention; however, their practical deployment is severely limited by the limited battery capacity, high cost of acoustic communications and difficulty of node maintenance. In particular, inefficient clustering and routing strategies result in unbalanced energy consumption, premature failures of nodes and decreased network lifetime. To solve these problems, the purpose of this paper is to design an energy-efficient and scalable clustering and multi-hop routing framework for UWSNs that can extend the network lifetime while ensuring reliable data delivery. We propose a hybrid optimization approach that is named as MPA-HGSO, where Marine Predator Algorithm (MPA) is used for cluster head selection and cluster formation while Henry Gas Solubility Optimization (HGSO) is used to optimize the multi-hop routing paths. The proposed framework is tested with extensive simulations performed in the Matlab environment in three base station deployment scenarios with a network of 300 sensor nodes deployed in a 200 × 200 m2 area. Performance is measured in terms of network lifetime, energy consumption, and end-to-end transmission delay and compared with LEACH, TEEN, MPSO, and IPSO-GWO protocols. Simulation results show that MPA-HGSO is significantly better than benchmark methods. In the central base station scenario, the proposed approach gives a First Node Die (FND) at 2151 rounds and a Half Nodes Die (HND) at 2160 rounds, as compared to 1115 and 1290 rounds for LEACH, respectively. Moreover, the average transmission delay is reduced to 140 ms, which is a reduction of up to 44% compared to conventional approaches. These results validate that the proposed MPA-HGSO framework is an effective energy consumption and network lifetime and communication efficiency balance framework, which is a promising solution for the long-term and large-scale UWSN deployments.