Dr. Sajal Das, PI on the SIRAC team, among others, publish research paper regarding maximizing the useful data collected by UAV based sensors. The paper was published in the 19th IEEE International Conference on Pervasive Computing and Communications. March 2021. The abstract can be seen below.
Data collection in Wireless Sensor Networks (WSNs) has been a hot research topic owing to the accelerated development in the Internet of Things (IoT). With high agility, mobility and flexibility, the Unmanned Aerial Vehicle (UAV) is widely considered as a promising technology for data collection in WSNs. Under the one-to-many data collection scheme, where a UAV is able to collect data from multiple sensors simultaneously within its reception range, the identification of hovering locations of the UAV impacts the efficiency of data collection significantly. Most existing studies either neglect this critical issue or discretize the UAV serving area into small regions with a given size, which results in the inevitable utility loss of data collection. In this paper, we jointly consider the hovering location positioning of the UAV and the utility maximization of data collection. Specifically, we first formulate a novel data collection utility maximization problem (UMP) and show that it is an NP-hard problem. We then devise an efficient algorithm for precisely positioning (potential) UAV hovering locations, which improves the data collection utility significantly. We also propose an approximation algorithm for UMP with approximation ratio (1−1e), where e is the base of the natural logarithm. We finally evaluate the performance of the proposed algorithms through simulation experiments, and demonstrate that the proposed algorithms significantly outperform four heuristics.
M. Chen, W. Liang, and S. K. Das, “The Utility Maximization for Data Collection in UAV-enabled Sensor Networks via Judiciously Determining Hovering Locations,” 19th IEEE International Conference on Pervasive Computing and Communications (PerCom), Mar 2021.