eprintid: 19878 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/98/78 datestamp: 2024-06-04 14:19:36 lastmod: 2024-06-04 14:19:36 status_changed: 2024-06-04 14:16:06 type: article metadata_visibility: show creators_name: Shukla, S. creators_name: Hussain, S. creators_name: Irshad, R.R. creators_name: Alattab, A.A. creators_name: Thakur, S. creators_name: Breslin, J.G. creators_name: Hassan, M.F. creators_name: Abimannan, S. creators_name: Husain, S. creators_name: Jameel, S.M. title: Network analysis in a peer-to-peer energy trading model using blockchain and machine learning ispublished: pub keywords: Autonomous agents; Energy utilization; Power markets; Reinforcement learning; Simulation platform; Smart meters; Smart power grids, Block-chain; Cloud-computing; Cyber security; Energy; Latency; Machine-learning; Neural-networks; Peer to peer; Peer-to-peer energy trade; Q-learning; Reinforcement learnings; Smart grid, Blockchain note: cited By 3 abstract: Existing technology like smart grid (SG) and smart meters play a significant role in meeting the everlasting demand of energy consumption, supply, and generation for peer-to-peer (P2P) energy trading between different distributed prosumers. Whereas blockchain when used with P2P energy trading plays a major role in cost and security by eliminating any involvement of outsiders and third parties. However, existing works related to the blockchain with P2P energy trading are engaged in increasing the cost related to resource allocation, latency, computational processing, and large network setup. The objective of this paper is to design and develop a three-tier architecture, an analytical model, and a hybrid algorithm for network analysis in a blockchain-based P2P energy trading system using reinforcement learning (RL) and feed forward neural network (FFNN) techniques. In this model, we will examine the various parameters and tradeoffs which affect the delay, throughput, and security in P2P energy trading. This will lead to profitable P2P energy trading between different distributed prosumers. By analyzing the simulation results of the proposed model and algorithm by benchmarking with the existing state-of-the-art techniques it's clear that the proposed algorithm shows marked improvement over network latency generated results. The simulation of the model is conducted using the iFogSim simulator, Ganache with Ethereum platform, Truffle, Python editor tool, and ATOM IDE with solidity. © 2023 Elsevier B.V. date: 2024 official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175193267&doi=10.1016%2fj.csi.2023.103799&partnerID=40&md5=a357b85aa2d5df62df1aa640bcf1302f id_number: 10.1016/j.csi.2023.103799 full_text_status: none publication: Computer Standards and Interfaces volume: 88 refereed: TRUE citation: Shukla, S. and Hussain, S. and Irshad, R.R. and Alattab, A.A. and Thakur, S. and Breslin, J.G. and Hassan, M.F. and Abimannan, S. and Husain, S. and Jameel, S.M. (2024) Network analysis in a peer-to-peer energy trading model using blockchain and machine learning. Computer Standards and Interfaces, 88.