LbSP : Load-Balanced Secure and Private Autonomous Electric Vehicle Charging Framework With Online Price Optimization
(2022) In IEEE Internet of Things Journal 9(17). p.15685-15696- Abstract
Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs' charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP)... (More)
Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs' charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP) for secure, private, and efficient EV charging with a minimal negative effect on the existing power grid. It assures reliable and efficient charging services by a lightweighted encryption technique. Also, it balances the energy consumption of power grids via an online pricing strategy that minimizes load variance by optimizing energy prices in real time. Moreover, it preserves users' privacy while not affecting online pricing using an advanced differential privacy technique. Furthermore, LbSP deploys on an edge-cloud structure for fast response and more precise pricing, where clouds balance overall load consumption by online price optimization while edges gather data for clouds and respond to charging requests from EVs. The evaluation results show that the proposed framework ensures secure and private EV charging, balances energy load consumption, and preserves users' privacy.
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- author
- Yuan, Yachao LU ; Yuan, Yali ; Memarmoshrefi, Parisa ; Baker, Thar and Hogrefe, Dieter
- organization
- publishing date
- 2022-09-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Autonomous electric vehicles (AEVs), differential privacy (DP), load balance, online price optimization (OPO), secure and private EV charging
- in
- IEEE Internet of Things Journal
- volume
- 9
- issue
- 17
- pages
- 12 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85125346940
- ISSN
- 2327-4662
- DOI
- 10.1109/JIOT.2022.3153271
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: This work was supported by the China Scholarship Council under Grant 201706050095. Publisher Copyright: © 2014 IEEE.
- id
- 416712d7-4cb6-4cd8-b228-49d23b8b3297
- date added to LUP
- 2022-12-29 14:28:28
- date last changed
- 2025-04-04 14:41:50
@article{416712d7-4cb6-4cd8-b228-49d23b8b3297,
abstract = {{<p>Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs' charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP) for secure, private, and efficient EV charging with a minimal negative effect on the existing power grid. It assures reliable and efficient charging services by a lightweighted encryption technique. Also, it balances the energy consumption of power grids via an online pricing strategy that minimizes load variance by optimizing energy prices in real time. Moreover, it preserves users' privacy while not affecting online pricing using an advanced differential privacy technique. Furthermore, LbSP deploys on an edge-cloud structure for fast response and more precise pricing, where clouds balance overall load consumption by online price optimization while edges gather data for clouds and respond to charging requests from EVs. The evaluation results show that the proposed framework ensures secure and private EV charging, balances energy load consumption, and preserves users' privacy.</p>}},
author = {{Yuan, Yachao and Yuan, Yali and Memarmoshrefi, Parisa and Baker, Thar and Hogrefe, Dieter}},
issn = {{2327-4662}},
keywords = {{Autonomous electric vehicles (AEVs); differential privacy (DP); load balance; online price optimization (OPO); secure and private EV charging}},
language = {{eng}},
month = {{09}},
number = {{17}},
pages = {{15685--15696}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Internet of Things Journal}},
title = {{LbSP : Load-Balanced Secure and Private Autonomous Electric Vehicle Charging Framework With Online Price Optimization}},
url = {{http://dx.doi.org/10.1109/JIOT.2022.3153271}},
doi = {{10.1109/JIOT.2022.3153271}},
volume = {{9}},
year = {{2022}},
}