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Author:

Guo, Jianxiong (Guo, Jianxiong.) | Ding, Xingjian (Ding, Xingjian.) | Wu, Weili (Wu, Weili.) | Du, Ding-zhu (Du, Ding-zhu.)

Indexed by:

EI Scopus SCIE

Abstract:

Electric Vehicles (EVs) are becoming more and more popular in our daily life, which replaces traditional fuel vehicles to reduce carbon emissions and protect the environment. EVs need to be charged, but the number of charging piles in a Charging Station (CS) is limited, and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on a Directed Acyclic Graph (DAG)-blockchain and double-auction mechanism. In a smart area, it attempts to assign EVs to the available CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalability during performing scheduling and completing tradings. In this process, a constrained multi-item double-auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve truthfulness as well as system efficiency compared to the existing double-auction model. To adapt to it, we propose two algorithms, namely, Truthful Mechanism for Charging (TMC) and Efficient Mechanism for Charging (EMC), to determine an assignment between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms.

Keyword:

truthfulness Electric vehicle (EV) DAG-based blockchain charging scheduling constrained multi-item double auction system efficiency

Author Community:

  • [ 1 ] [Guo, Jianxiong]Beijing Normal Univ, Adv Inst Nat Sci, Zhuhai, Peoples R China
  • [ 2 ] [Guo, Jianxiong]BNU HKBU United Int Coll, Guangdong Key Lab AI & Multimodal Data Proc, Zhuhai, Peoples R China
  • [ 3 ] [Ding, Xingjian]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 4 ] [Wu, Weili]Univ Texas Dallas, Dept Comp Sci, Richardson, TX USA
  • [ 5 ] [Du, Ding-zhu]Univ Texas Dallas, Dept Comp Sci, Richardson, TX USA

Reprint Author's Address:

  • [Ding, Xingjian]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China;;

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Source :

ACM TRANSACTIONS ON SENSOR NETWORKS

ISSN: 1550-4859

Year: 2024

Issue: 5

Volume: 20

Page: 1-27

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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