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Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks
Joint Authors
Van, Tien Pham
Van, Nguyen Pham
Duyen, Trung Ha
Source
International Journal of Aerospace Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-16
Country of Publication
Egypt
No. of Pages
20
Abstract EN
Increasingly inexpensive unmanned aerial vehicles (UAVs) are helpful for searching and tracking moving objects in ground events.
Previous works either have assumed that data about the targets are sufficiently available, or they solely rely on on-board electronics (e.g., camera and radar) to chase them.
In a searching mission, path planning is essentially preprogrammed before taking off.
Meanwhile, a large-scale wireless sensor network (WSN) is a promising means for monitoring events continuously over immense areas.
Due to disadvantageous networking conditions, it is nevertheless hard to maintain a centralized database with sufficient data to instantly estimate target positions.
In this paper, we therefore propose an online self-navigation strategy for a UAV-WSN integrated system to supervise moving objects.
A UAV on duty exploits data collected on the move from ground sensors together with its own sensing information.
The UAV autonomously executes edge processing on the available data to find the best direction toward a target.
The designed system eliminates the need of any centralized database (fed continuously by ground sensors) in making navigation decisions.
We employ a local bivariate regression to formulate acquired sensor data, which lets the UAV optimally adjust its flying direction, synchronously to reported data and object motion.
In addition, we also construct a comprehensive searching and tracking framework in which the UAV flexibly sets its operation mode.
As a result, least communication and computation overhead is actually induced.
Numerical results obtained from NS-3 and Matlab cosimulations have shown that the designed framework is clearly promising in terms of accuracy and overhead costs.
American Psychological Association (APA)
Van, Tien Pham& Van, Nguyen Pham& Duyen, Trung Ha. 2020. Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks. International Journal of Aerospace Engineering،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1167974
Modern Language Association (MLA)
Van, Tien Pham…[et al.]. Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks. International Journal of Aerospace Engineering No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1167974
American Medical Association (AMA)
Van, Tien Pham& Van, Nguyen Pham& Duyen, Trung Ha. Self-Navigating UAVs for Supervising Moving Objects over Large-Scale Wireless Sensor Networks. International Journal of Aerospace Engineering. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1167974
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1167974