Abstract The underwater Internet of things (UIoT) has become a prominent technology in next-generation ocean monitoring systems, with autonomous underwater vehicles (AUVs) serving as critical mobile edge sensing platforms for sensor network data collection. The deployment and application of 5 G technology in UIoT mark a significant advancement in underwater network communication capabilities. However, the performance of UIoT is critically constrained by two fundamental challenges: the dynamic and unpredictable nature of underwater environments, and the energy limitations of AUV serving as mobile edge nodes. For instance, node mobility caused network instability, affecting data collection efficiency. And high and uneven energy consumption leads to shortened network lifetime. Moreover, limited AUV energy results in AUV loss and diminished data collection efficiency. To solve these problems, an energy-efficient optimization data collection algorithm based on mobile edge sensing in 5 G UIoT (EEODC-MES) is proposed in this paper. In EEODC-MES, the network clustering is constructed by analyzing the movement characteristics of sensor nodes, and a cluster-head node is selected. Subsequently, the reward for edge sensing device (AUV) collecting data from cluster-head nodes is calculated based on the payoff matrix. The cluster-head node with the highest reward value is prioritized for AUV visitation. AUV decides whether to continue visiting cluster nodes or return to base based on its remaining energy and return energy consumption. The performance of EEODC-MES is compared with that of other data collection algorithms, namely greedy and adaptive AUV path-finding (GAAP), AUV-aided energy-efficient data collection (AEEDCO), and traveling salesperson problem (TSP). Compared with GAAP, AEEDCO, and TSP, EEODC-MES, respectively, improves the network lifetime by 31.8%, 30.1%, and 7.1%. Compared with GAAP and TSP, EEODC-MES, respectively, reduces the collection delay by 26.08% and 51.77%.
Guang et al. (Sat,) studied this question.