Qin, Yujie, Kishk, Mustafa A. and Alouini, Mohamed-Slim (2023) Performance Analysis of Charging Infrastructure Sharing in UAV and EV-Involved Networks. IEEE Transactions on Vehicular Technology, 72 (3). pp. 3973-3988. ISSN 0018-9545
Preview
MK_performance.pdf
Download (2MB) | Preview
Abstract
Electric vehicles (EVs) and unmanned aerial vehicles (UAVs) show great potential in modern transportation and communication networks, respectively. However, with growing demands for such technologies, the limited energy infrastructure becomes the bottleneck for their future growth. It might be of high cost and low energy efficiency for all the operators to each have their own dedicated energy infrastructure, such as charging stations. In this paper, we analyze a wireless charging infrastructure sharing strategy in UAV and EV-involved networks. We consider a scenario where UAVs can charge in EV charging stations and pay for the sharing fee. On the EVs' side, sharing infrastructure can earn extra profit but their service quality, such as waiting time, might slightly reduce. On the UAVs' side, if renting EV charging stations can achieve an acceptable system performance, say high coverage probability, while considering the cost, they may not need to build their dedicated charging stations. In this case, we use tools from stochastic geometry to model the locations and propose an optimization problem that captures the aforementioned trade-offs between cost or profit and quality of service. Our numerical results show that sharing infrastructure slightly increases the waiting time of EVs, say within 5 min, but dramatically decreases the waiting time of drones, say more than 50 min, and deploying more charging stations do achieve better performances, but all these better performances are expected to cost more.
Item Type: | Article |
---|---|
Keywords: | Stochastic geometry; poisson point process; electric vehicles; unmanned aerial vehicles; infrastructure sharing; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: | 19394 |
Identification Number: | 10.1109/TVT.2022.3219764 |
Depositing User: | Mustafa Kishk |
Date Deposited: | 21 Jan 2025 16:25 |
Journal or Publication Title: | IEEE Transactions on Vehicular Technology |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/19394 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year