A Secure and Efficient Energy Trading Model Using Blockchain for a 5G-Deployed Smart Community

A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of the potential privacy and security challenges in the distributed energy system, it is becoming a grea...

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Veröffentlicht in:Wireless communications and mobile computing 2022-01, Vol.2022, p.1-27
Hauptverfasser: Yahaya, Adamu Sani, Javaid, Nadeem, Ullah, Sameeh, Khalid, Rabiya, Javed, Muhammad Umar, Khan, Rehan Ullah, Wadud, Zahid, Khan, Muhammad Asghar
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container_end_page 27
container_issue
container_start_page 1
container_title Wireless communications and mobile computing
container_volume 2022
creator Yahaya, Adamu Sani
Javaid, Nadeem
Ullah, Sameeh
Khalid, Rabiya
Javed, Muhammad Umar
Khan, Rehan Ullah
Wadud, Zahid
Khan, Muhammad Asghar
description A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of the potential privacy and security challenges in the distributed energy system, it is becoming a great problem to optimally schedule EVs’ charging with different energy consumption patterns and perform reliable energy trading in the SC. In this paper, a blockchain-based privacy-preserving energy trading system for 5G-deployed SC is proposed. The proposed system is divided into two components: EVs and residential prosumers. In this system, a reputation-based distributed matching algorithm for EVs and a Reward-based Starvation Free Energy Allocation Policy (RSFEAP) for residential homes are presented. A short-term load forecasting model for EVs’ charging using multiple linear regression is proposed to plan and manage the intermittent charging behavior of EVs. In the proposed system, identity-based encryption and homomorphic encryption techniques are integrated to protect the privacy of transactions and users, respectively. The performance of the proposed system for EVs’ component is evaluated using convergence duration, forecasting accuracy, and executional and transactional costs as performance metrics. For the residential prosumers’ component, the performance is evaluated using reward index, type of transactions, energy contributed, average convergence time, and the number of iterations as performance metrics. The simulation results for EVs’ charging forecasting gives an accuracy of 99.25%. For the EVs matching algorithm, the proposed privacy-preserving algorithm converges faster than the bichromatic mutual nearest neighbor algorithm. For RSFEAP, the number of iterations for 50 prosumers is 8, which is smaller than the benchmark. Its convergence duration is also 10 times less than the benchmark scheme. Moreover, security and privacy analyses are presented. Finally, we carry out security vulnerability analysis of smart contracts to ensure that the proposed smart contracts are secure and bug-free against the common vulnerabilities’ attacks. The results show that the smart contracts are secure against both internal and external attacks.
doi_str_mv 10.1155/2022/6953125
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subjects Algorithms
Benchmarks
Blockchain
Charging
Communication
Consumers
Contracts
Convergence
Cryptography
Distributed generation
Electric vehicles
Emission standards
Emissions
Energy consumption
Energy management
Forecasting
Free energy
Greenhouse gases
Matching
Mathematical models
Neural networks
Optimization techniques
Peer to peer computing
Performance evaluation
Performance measurement
Privacy
Renewable energy sources
Security
Smart grid
Smart houses
title A Secure and Efficient Energy Trading Model Using Blockchain for a 5G-Deployed Smart Community
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