Privacy-Preserving Routing and Charging Scheduling for Cellular-Connected Unmanned Aerial Vehicles

Liu, B; Ni, W; Liu, RP; Guo, YJ; Zhu, H

Privacy-Preserving Routing and Charging Scheduling for Cellular-Connected Unmanned Aerial Vehicles

Liu, B Ni, W Liu, RP Guo, YJ Zhu, H

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54, (8), pp. 4929-4941
Issue Date:: 2024-01-01

Closed Access

	Filename	Description	Size
	1726902.pdf	Published version	1.54 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, B
dc.contributor.author	Ni, W
dc.contributor.author	Liu, RP
dc.contributor.author	Guo, YJ
dc.contributor.author	Zhu, H
dc.date.accessioned	2024-08-07T05:27:41Z
dc.date.available	2024-08-07T05:27:41Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024, 54, (8), pp. 4929-4941
dc.identifier.issn	2168-2216
dc.identifier.issn	2168-2232
dc.identifier.uri	http://hdl.handle.net/10453/180315
dc.description.abstract	Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedules in a decentralized fashion. The framework allows each UAV to minimize its energy usage and connectivity outage, maximize its recharged energy, ensure its timely arrival, and preserve its privacy concerning its trajectory and destination. The key idea is that we obfuscate probabilistically the destination of each UAV, and design a new noncooperative Bayesian game among the UAVs to find their best routes and charging schedules toward the obfuscated destinations. Another important aspect is that we prove the game is a potential Bayesian game with a pure-strategy Bayesian Nash equilibrium and the best response yielded with the Bellman-Ford algorithm. This new framework preserves the UAVs' privacy in the sense that an UAV only shares the probability of its visit to a charging station at different times, and its best response is based on an obfuscated destination. Simulations demonstrate that the framework ensures timely arrivals with near-optimal routes and substantially lower complexity than a centralized routing scheme based on brute force. 2168-2216
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.relation.isbasedon	10.1109/TSMC.2024.3392011
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Privacy-Preserving Routing and Charging Scheduling for Cellular-Connected Unmanned Aerial Vehicles
dc.type	Journal Article
utslib.citation.volume	54
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Global Big Data Technologies Research Centre (GBDTC)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T05:27:39Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	54
utslib.citation.issue	8

Abstract:

Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedules in a decentralized fashion. The framework allows each UAV to minimize its energy usage and connectivity outage, maximize its recharged energy, ensure its timely arrival, and preserve its privacy concerning its trajectory and destination. The key idea is that we obfuscate probabilistically the destination of each UAV, and design a new noncooperative Bayesian game among the UAVs to find their best routes and charging schedules toward the obfuscated destinations. Another important aspect is that we prove the game is a potential Bayesian game with a pure-strategy Bayesian Nash equilibrium and the best response yielded with the Bellman-Ford algorithm. This new framework preserves the UAVs' privacy in the sense that an UAV only shares the probability of its visit to a charging station at different times, and its best response is based on an obfuscated destination. Simulations demonstrate that the framework ensures timely arrivals with near-optimal routes and substantially lower complexity than a centralized routing scheme based on brute force. 2168-2216

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180315