Environment and Energy-Aware AUV-Assisted Data Collection for the Internet of Underwater Things

Considering the wide-area distribution and limited transmission power of sensing devices in the Internet of Underwater Things (IoUT), employing autonomous underwater vehicles (AUVs) to collect data is considered a promising solution. While most existing AUV-assisted data collection schemes primarily focus on enhancing data collection throughput and identifying the shortest path, they often overlook the influence of the underwater environment on AUV and the timeliness of data collection. In this paper, we design a multi-AUV-assisted data collection system, in which AUVs select their own target devices to collect data according to the data upload urgencies of IoUT devices. Considering the disturbance of turbulent ocean environment and the limited energy of AUV, we propose an environment and energy-aware AUV-assisted data collection scheme. This scheme aims to conduct path planning for multiple AUVs based on perceived environmental information, including turbulent fields and device statuses. The primary goals are to maximize the sum data collection rate and total data throughput, minimize AUV energy consumption, reduce the average data overflow times. To solve this high-dimensional NP-hard problem, we first model the problem as a Markov decision process, and propose a multi-agent independent soft actor-critic to solve it. Extensive simulations validate the effectiveness and adaptability of our approach.