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Secured data transmissions in corporeal unmanned device to device using machine learning algorithm
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Shitharth, S., Yonbawi, Saud, Manoharan, Hariprasath, Shankar, Achyut, Maple, Carsten and Alahmari, Sultan (2023) Secured data transmissions in corporeal unmanned device to device using machine learning algorithm. Physical Communication, 59 . 102116. doi:10.1016/j.phycom.2023.102116 ISSN 18744907.
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Official URL: https://doi.org/10.1016/j.phycom.2023.102116
Abstract
Cyber–physical systems (CPS) for device-to-device (D2D) communications are gaining prominence in today’s sophisticated data transmission infrastructures. This research intends to develop a new model for UAV transmissions across distinct network nodes, which is necessary since an automatic monitoring system is required to enhance the current D2D application infrastructure. The real time significance of proposed UAV for D2D communications can be observed during data transmission state where individual data will have huge impact on maximizing the D2D security. Additionally, through the use of simulation, an exploratory persistence tool is offered for CPS networks with fully characterized energy issues. This UAV CPS paradigm is based on mobility nodes, which host concurrent systems and control algorithms. In sixth-generation networks, when there are no barriers and the collision rate is low and the connectivity is fast, the method is also feasible. Unmanned aerial vehicles (UAVs) can now cover great distances, even while encountering hazardous obstacles. When compared to the preexisting models, the simulated values for autonomous, collision, and parametric reliability are much better by an average of 87%. The proposed model, however, is shown to be highly independent and exhibits stable perceptual behaviour. The proposed UAV approach is optimal for real-time applications due to its potential for more secure operation via a variety of different communication modules.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Library of Congress Subject Headings (LCSH): | Drone aircraft , Wireless communication systems, Drone aircraft -- Computer networks, Machine-to-machine communications, Data transmission systems, Cooperating objects (Computer systems) , 6G mobile communication systems , Machine learning -- Mathematical models | ||||||||
Journal or Publication Title: | Physical Communication | ||||||||
Publisher: | Elsevier | ||||||||
ISSN: | 18744907 | ||||||||
Official Date: | August 2023 | ||||||||
Dates: |
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Volume: | 59 | ||||||||
Article Number: | 102116 | ||||||||
DOI: | 10.1016/j.phycom.2023.102116 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 5 September 2023 |
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