Energy-efficient Optimization Data Collection Algorithm based on Mobile Edge Sensing in 5G Underwater Internet of Things

Abstract Underwater Internet of Things (UIoT) has emerged as one of the prominent technologies in the development of future ocean monitoring systems, where mobile edge sensing devices (such as autonomous underwater vehicles (AUVs)) provide a promising approach for data collection from sensor nodes. The deployment of 5G technology in UIoT signifies a significant advancement in underwater network communication capabilities. However, UIoT is severely affected by the underwater dynamic environment and the limited energy of AUV. 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 this problems, an energy-efficient optimization data collection algorithm based on mobile edge sensing in 5G underwater internet of things (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. The performance of EEODC-MES is compared with that of other data collection algorithms, namely GAAP, AEEDCO, and 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%.