Dynamic data collection algorithm based on mobile edge computing in underwater internet of things

Xiaoyun Guang, Chunfeng Liu, Wenyu Qu, Zhao Zhao

Abstract: The Underwater Internet of Things (UIoT) has emerged as one of the prominent technologies in the development of future ocean monitoring systems, where mobile edge elements (such as autonomous underwater vehicles (AUVs)) provide a promising method for the data collection from sensor nodes. However, as an important part of the UIoT, underwater wireless sensor networks (UWSNs) are severely affected by the underwater dynamic environment. For instance, node locations change continuously, which significantly increases the difficulty of data collection. To solve this problem, the concept of an inevitable communication space (ICS) is proposed. The ICS is calculated by analyzing the variation in the position of nodes and the communication range. Furthermore, an ICS-based dynamic data collection algorithm (ICS-DDCA) for UIoT is proposed to collect underwater data. This method utilizes the ICS instead of the initial location of the node for data collection to further improve the performance of the algorithm and shorten the data collection time. The simulation results demonstrate that compared with the energy-efficient data collection over AUV-assisted (EEDA) and data collection algorithms based on probabilistic neighborhood (PNCS-GHA), ICS-DDCA can effectively reduce the collection time, while ensuring the full completion of data collection.