Title

A Federated Learning and Blockchain-enabled Sustainable Energy-Trade at the Edge: A Framework for Industry 4.0

Document Type

Article

Source of Publication

IEEE Internet of Things Journal

Publication Date

1-1-2022

Abstract

Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and AI, enables smart and secure micro-grid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end-devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and Federated Learning-enabled solution provides secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

PP

Issue

99

Disciplines

Computer Sciences

Keywords

Blockchains, Quality of service, Internet of Things, Computational modeling, Task analysis, Peer-to-peer computing, Adaptation models

Indexed in Scopus

no

Open Access

no

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