Collect Spatiotemporally Correlated Data in IoT Networks With an Energy-Constrained UAV

• Published in: IEEE internet of things journal 2024-06, Vol.11 (11), p.20486-20498
• Main Authors: Xu, Wenzheng, Shao, Heng, Shen, Qunli, Peng, Jian, Huang, Wen, Liang, Weifa, Liu, Tang, Yao, Xin-Wei, Lin, Tao, Das, Sajal K.
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation algorithms; Autonomous aerial vehicles; Clustering algorithms; Clusters; Correlation; Data collection; Internet of Things; mobile data collections; Sensors; spatial data correlations; Spatial databases; Spatiotemporal data; Unmanned aerial vehicles; unmanned aerial vehicles (UAVs)
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3370295

Unmanned aerial vehicles (UAVs) are promising tools for efficient data collections of sensors in Internet of Things networks. Existing studies exploited both spatial and temporal data correlations to reduce the amount of collected redundant data, in which sensors are first partitioned into different clusters, a master sensor in each cluster then collects raw data from other sensors and compresses the received data. An energy-constrained UAV finally collects the maximum amount of compressed data from different master sensors. We however notice that the compressed data from only a portion of clusters are collected by the UAV in the existing studies, while the data from other clusters are not collected at all. In this article, we study a problem of finding a data collection trajectory for an energy-constrained UAV, so that the accumulative utility of collected data is maximized, where the accumulative utility measures the quality of spatiotemporally correlated data collected from different clusters. We propose a novel {} [{1}/({6+\epsilon })] -approximation algorithm for the problem, where \epsilon is a given constant with \epsilon > 0 . Experimental results with real data sets show that the accumulative utility by the proposed algorithm is at least 23% larger than those by the existing studies, and the number of clusters collected by the proposed algorithm is from 45% to 105% larger than those by the existing studies.