Journal Special Issue: Machine Learning for Blockchain-Based IoT Systems

We are organising a special issue of Elsevier IoT Journal on “Machine Learning for Blockchain-Based IoT Systems: Architecture, Algorithms, and Applications” The full details are below. Please follow this link to access the issue on Elsevier’s page.

Aims and Scope:

Machine Learning (ML) technology embraces machines to learn, think and make intelligent decisions autonomously. The fundamental approach of ML is to build efficient algorithms that are capable of predicting the future learned through experience. Blockchain, on the other hand, is distributed ledger technology that is immutable, decentralized, and provides secure storage of data without the need of a trusted third party. The convergence of ML and blockchain will complement each other to produce a greater impact and availability of different services including, healthcare, supply chain, transportation, and power sectors. These services include a large number of network elements and edge devices that generate a huge amount of data that raise potential security concerns and data optimization issues. Further, with the emergence of the Internet of Things (IoT), the nature of interactions and attacks has become more sophisticated to generate falsified identities and control over the blockchain consensus. Traditional methods use a signature-based approach to determine the specific attack patterns, however, to provide more robust, transparent, efficient, and intelligent methods to detect attack patterns and efficient data optimization – ML technology is a promising approach. Moreover, the convergence of ML in blockchain has emerged to tackle large-scale IoT services towards improving uncertainty, computational efficiency, and cost reduction.

However, the major challenges for ML adaptation in blockchain are, (i) the design and development of smart agents with improved learning capability to regulate blockchain and consider the uncertainty present in the network (for example in IoT), (ii) to evaluate the performance of learning-based analysis model for blockchain-based systems, (iii) to combine ML-assisted data fusion mechanisms for multi-layer and multi-vendor blockchain systems for data authorization, (iv) implementing ML algorithms in smart contracts, and (iv) expansion of ML-assisted supervised learning scheme for cryptocurrency trading especially in a public blockchain. To provide a secure, transparent, scalable, and trustworthy blockchain-based IoT supported by ML demands novel, methodological, theoretical, algorithmic, and mathematical solutions. This further calls for better advocating intelligent and deterministic execution of applications to create a fertile ground for research and innovation.

The purpose of this special issue is to discover and promote the current advancements, techniques, innovation, and real-world solutions of ML techniques in blockchain-based IoT infrastructure. This special issue will focus on gathering both quantitative and qualitative research contributions from individual, academic, organizational and industry practitioners in the wider area of ML and blockchain solutions for resource management, scalable operation, big data processing, and security issues.


Topics of interest to the Special Issue include but not limited to:

  • Novel ML methods, techniques, theories, and services for blockchain-based IoT systems
  • Architecture for convergence of blockchain-based IoT supported by ML techniques
  • Use of ML techniques for security, trust, and privacy in blockchain-based IoT systems
  • Blockchain and ML assisted autonomous recommendations systems for IoT
  • Defensive policy management based on ML techniques for securing blockchain-based IoT systems
  • Lightweight and secure ML algorithms for blockchain-assisted supply chain applications
  • Other technologies and applications that advocate ML techniques in the field of blockchain and IoT

Important Dates:

Manuscripts submission due: 30 August 2021

1st Review Notification to authors due: 30 November 2021

Revised Manuscript due: 30 January 2022

2nd Review Notification to authors due: 30 March 2022

Final notification due: 20 May 2022

Notes for Prospective Authors

See Guide for Authors


All papers must be submitted online.

Submit your paper:

Guest Editors

Name: Dr. Shantanu Pal (Lead Guest Editor)

Institution: Queensland University of Technology

Country: Brisbane, Australia



Name: Prof. Raja Jurdak

Institution: Queensland University of Technology

Country: Brisbane, Australia



Name: Prof. Bhaskar Krishnamachari

Institution: University of Southern California

Country: Loa Angeles, USA



Name: Prof. Rasheed Hussain

Institution: Innopolis University

Country: Tatarstan, Russia


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