Energy-Efficient Multiple Autonomous Underwater Vehicle Path Planning Scheme in Underwater Sensor Networks

Abstract

The issue of limited energy resources is crucial for underwater wireless sensor networks (UWSNs) because these networks operate in remote and harsh environments where access to power sources is limited. Overcoming the energy constraints is necessary to ensure the long-term functionality and sustainability of UWSN, enabling continuous data collection and communication for various applications such as environmental monitoring and surveillance. To solve the problems of limited energy and the difficulty of battery replacement in UWSN, a path planning and energy-saving scheme for charging underwater sensor nodes using AUVs (autonomous underwater vehicles) is proposed. Applying multiple AUVs to charge the sensing network nodes will maximize the size of the underwater sensing network as well as meet the transmission reliability, and the optimal path of AUVs is solved by using a genetic algorithm. Simulation results show that the AUV path planning scheme convergence is faster than that of conventional algorithms, and the lifetime of UWSN is prolonged while energy balancing according to the network size and node density. In high-density networks, the average energy consumption generated by AUVs for exploration is reduced by 15 percent for each additional AUV with our path planning.