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Consensus Mechanism for Blockchain-Enabled Vehicle-to-Vehicle Energy Trading in the Internet of Electric Vehicles

计算机科学 验证器 计算机网络 可扩展性 数字加密货币 块链 分布式计算 计算机安全 数据库 万维网
作者
Hayla Nahom Abishu,Abegaz Mohammed Seid,Yasin Habtamu Yacob,Tewodros Alemu Ayall,Guolin Sun,Guisong Liu
出处
期刊:IEEE Transactions on Vehicular Technology [Institute of Electrical and Electronics Engineers]
卷期号:71 (1): 946-960 被引量:50
标识
DOI:10.1109/tvt.2021.3129828
摘要

Electric Vehicles (EVs) have emerged as one of the most promising solutions for reducing carbon emissions in smart cities. However, due to the limited battery life of EVs and the scarcity of charging stations, EV drivers are not willing to travel long distances. Thus, blockchain-enabled energy trading (BET) has lately been used to securely share energy among EVs via wireless power transfer (WPT) technology. Blockchain is used to ensure the security and privacy of transactions between untrustworthy EVs in the WPT process. Nevertheless, previous works on BET have relied on existing consensus mechanisms built on the requirements of the cryptocurrency systems. These consensus mechanisms have faced significant challenges in maintaining high reliability, throughput, low latency, and network scalability in V2V energy trading that requires real-time services. To address these issues, we propose a new consensus mechanism that leverages the benefits of Practical Byzantine Fault Tolerance (PBFT) and Proof of Reputation (PoR) called PBFT-based PoR (PPoR). The energy trading process runs in a clustered vehicular network, where validator selection, block generation, and consensus processes are performed in each cluster. We adopt an incentive mechanism based on a Stackelberg game model to optimize the utility of sellers, buyers, and validator nodes, which motivates honest and cooperative nodes. The simulation results show that the proposed scheme reduces buyers’ costs by 21.1% while increasing the utility of sellers by 18%. Moreover, compared to benchmarks, the proposed scheme reduces the transaction processing delay and increases the throughput by more than 47.1% and 15.7%, respectively.
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