Data Collection Utility Maximization in Wireless Sensor Networks via Efficient Determination of UAV Hovering Locations
Abstract
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 - 1/e), 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.
Recommended Citation
M. Chen et al., "Data Collection Utility Maximization in Wireless Sensor Networks via Efficient Determination of UAV Hovering Locations," 2021 IEEE International Conference on Pervasive Computing and Communications, PerCom 2021, Institute of Electrical and Electronics Engineers (IEEE), Mar 2021.
The definitive version is available at https://doi.org/10.1109/PERCOM50583.2021.9439126
Meeting Name
2021 IEEE International Conference on Pervasive Computing and Communications, PerCom (2021: Mar. 22-26, Kassel, Germany)
Department(s)
Computer Science
Keywords and Phrases
Approximation Algorithms; Data Collection; Energy Efficiency; Internet of Things (IoT); Unmanned Aerial Vehicles (UAV); Utility Maximization; Wireless Sensor Networks (WSNs)
International Standard Book Number (ISBN)
978-166540418-1
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
22 Mar 2021
Comments
The work of Sajal K. Das is supported by the NSF grants CNS-2008878, SCC-IRG-1952045, and PPoSS-1725755.