AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform

Wang, Zhishang; Ikechukwu, Ogbodo Mark; Huang, Huakun; Qiu, Chen; Hisada, Masayuki; Abdallah, Abderazek Ben

doi:10.1109/access.2020.3044612

Cited by 68 publications

(40 citation statements)

References 49 publications

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“…A pricing platform was further given based on a consortium blockchain. Additionally, the FL-based power management was investigated by Wang et al [87]. They proposed an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS, for smart grid.…”

Section: Blockchain-enabled Incentive Mechanisms In Iotmentioning

confidence: 99%

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Wang

Leung

2021

Future Internet

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Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

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Section: Blockchain-enabled Incentive Mechanisms In Iotmentioning

confidence: 99%

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Wang

Leung

2021

Future Internet

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“…Though double auction method is used, there is no dynamic price fixation in this work. Z. Wang et al [8], proposed a method of power management based on Artificial intelligence. Artificial neural network is used for predicting the amount of energy in EV.…”

Section: Related Workmentioning

confidence: 99%

“…Total execution time is the time taken to complete the total trading process. The execution time taken by our proposed method and conventional method to complete the total process for different number of Electric vehicles is shown in the table 8.In order to calculate the execution time analysis, python 3.6.2 software is used for the practical experimental setup and algorithm is written in python code. The table 8 further highlights as the number of EVs increases, the time gap also increases between the proposed and conventional method.…”

Section: Figure 4 Comparison Of Computational Cost Of Different Schemesmentioning

confidence: 99%

A Secure and Decentralized Blockchain Based EV Energy Trading Model Using Smart Contract in V2G Network

Iqbal

Rajasekaran²,

Nikhil³

et al. 2021

IEEE Access

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In this work, a secure and decentralized Blockchain based energy trading model for electric vehicles (EVs) using Smart contract that achieves Peer-to-Peer (P2P) transactions between EVs in Vehicle to Grid networks is designed. The traditional energy trading model is a centralized structure based on trusted third parties, and there may an issue of single-point failure and leakage of privacy. In this way, a blockchainbased framework offers a secure, efficient and transparent trading model. Initially, the participating EVs and aggregator in the trading process should register at the trusted authority. Once the registration is successfully completed, both EVs and aggregator authenticate each other mutually in an anonymous manner. Moreover, only authorized EVs (charging and discharging EVs) participate in the contrary auction mechanism to exchange power/money based on their demand. Simulation conducted for the proposed scheme shows that our scheme has high speed (i.e., less computational time and execution time) which improves the market efficiency. In-addition, the transactions are non tamperable, when compared to the conventional scheme.

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“…To increase the prediction accuracy, the charging stations were clustered into different groups based on their location before performing the prediction. Similarly, Wang et al [61] used weather conditions, geography, characteristics of the vehicle, and driving style as inputs to a federated learning model to predict EV charging demand while considering the charging stations as agents.…”

Section: Demand Predictionmentioning

confidence: 99%

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Gholizadeh

Musilek

2021

Energies

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In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

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AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform

Cited by 68 publications

References 49 publications

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

A Secure and Decentralized Blockchain Based EV Energy Trading Model Using Smart Contract in V2G Network

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

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