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Future Internet
Special Issues
AI and Blockchain: Synergies, Challenges, and Innovations
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AI and Blockchain: Synergies, Challenges, and Innovations

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5274

Special Issue Editors

Dr. Klitos Christodoulou

E-Mail Website
Guest Editor
Department of Digital Innovation, University of Nicosia, Nicosia 2417, Cyprus
Interests: blockchain; distributed ledger technologies; data management
Special Issues, Collections and Topics in MDPI journals
Dr. Massimiliano Garda

E-Mail Website
Guest Editor
Department of Information Engineering (DII), University of Brescia, 25121 Brescia, Italy
Interests: big data exploration; smart cities; semantic web; data lake; blockchain; big data; business intelligence

Special Issue Information

Dear Colleague,

This Special Issue explores the intersection of artificial intelligence (AI) and blockchain technology, highlighting their combined potential to revolutionize various industries. The issue brings together cutting-edge research and innovative applications, providing insights into the synergies, challenges, and future directions of these transformative technologies.

Relevant research topics are as follows:

  1. AI for blockchain optimization: exploring how AI can enhance blockchain efficiency, scalability, and security through predictive analytics, machine learning, and optimization algorithms.
  1. Blockchain for AI trustworthiness: investigating how blockchain can ensure the integrity, transparency, and auditability of AI models and data, fostering trust in AI systems.
  1. Decentralized AI: examining decentralized AI applications where blockchain facilitates distributed learning, federated learning, and the secure sharing of AI models and data.
  1. Smart contracts and autonomous systems: analyzing the integration of AI with smart contracts to enable self-executing, autonomous systems in various domains, such as finance, the supply chain, and IoT.
  1. Data privacy and security: addressing the combined role of AI and blockchain in enhancing data privacy and security, including techniques for secure multiparty computation, differential privacy, and encrypted data analysis.
  1. Governance and ethical implications: discussing the ethical, legal, and governance issues arising from the convergence of AI and blockchain, including issues of bias, accountability, and regulatory compliance.
  1. AI-driven blockchain applications: showcasing innovative applications where AI drives the development of blockchain solutions, including healthcare, finance, logistics, and beyond.
  1. Interoperability and standards: investigating the challenges and solutions for achieving interoperability between AI systems and different blockchain platforms, along with the development of industry standards.

This Special Issue aims to provide a comprehensive overview of the current state of research, highlight novel approaches, and foster a multidisciplinary dialogue on the future landscape of AI and blockchain technologies.

Dr. Klitos Christodoulou
Dr. Massimiliano Garda
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • blockchain
  • smart contracts
  • distributed ledger

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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36 pages, 2113 KB  
Article
Self-Sovereign Identities and Content Provenance: VeriTrust—A Blockchain-Based Framework for Fake News Detection
by Maruf Farhan, Usman Butt, Rejwan Bin Sulaiman and Mansour Alraja
Future Internet 2025, 17(10), 448; https://doi.org/10.3390/fi17100448 - 30 Sep 2025
Viewed by 806
Abstract
The widespread circulation of digital misinformation exposes a critical shortcoming in prevailing detection strategies, namely, the absence of robust mechanisms to confirm the origin and authenticity of online content. This study addresses this by introducing VeriTrust, a conceptual and provenance-centric framework designed to [...] Read more.
The widespread circulation of digital misinformation exposes a critical shortcoming in prevailing detection strategies, namely, the absence of robust mechanisms to confirm the origin and authenticity of online content. This study addresses this by introducing VeriTrust, a conceptual and provenance-centric framework designed to establish content-level trust by integrating Self-Sovereign Identity (SSI), blockchain-based anchoring, and AI-assisted decentralized verification. The proposed system is designed to operate through three key components: (1) issuing Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) through Hyperledger Aries and Indy; (2) anchoring cryptographic hashes of content metadata to an Ethereum-compatible blockchain using Merkle trees and smart contracts; and (3) enabling a community-led verification model enhanced by federated learning with future extensibility toward zero-knowledge proof techniques. Theoretical projections, derived from established performance benchmarks, suggest the framework offers low latency and high scalability for content anchoring and minimal on-chain transaction fees. It also prioritizes user privacy by ensuring no on-chain exposure of personal data. VeriTrust redefines misinformation mitigation by shifting from reactive content-based classification to proactive provenance-based verification, forming a verifiable link between digital content and its creator. VeriTrust, while currently at the conceptual and theoretical validation stage, holds promise for enhancing transparency, accountability, and resilience against misinformation attacks across journalism, academia, and online platforms. Full article
(This article belongs to the Special Issue AI and Blockchain: Synergies, Challenges, and Innovations)
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47 pages, 3078 KB  
Article
Leveraging Blockchain for Ethical AI: Mitigating Digital Threats and Strengthening Societal Resilience
by Chibuzor Udokwu, Roxana Voicu-Dorobanțu, Abiodun Afolayan Ogunyemi, Alex Norta, Nata Sturua and Stefan Craß
Future Internet 2025, 17(7), 309; https://doi.org/10.3390/fi17070309 - 17 Jul 2025
Cited by 1 | Viewed by 2664
Abstract
This position paper proposes a conceptual framework (CF-BIAI-SXT) for integrating blockchain with AI to enhance ethical governance, transparency, and privacy in high-risk AI applications that ensure societal resilience through the mitigation of sexual exploitation. Sextortion is a growing form of digital sexual exploitation, [...] Read more.
This position paper proposes a conceptual framework (CF-BIAI-SXT) for integrating blockchain with AI to enhance ethical governance, transparency, and privacy in high-risk AI applications that ensure societal resilience through the mitigation of sexual exploitation. Sextortion is a growing form of digital sexual exploitation, and the role of AI in its mitigation and the ethical issues that arise provide a good case for this paper. Through a combination of systematic and narrative literature reviews, the paper first explores the ethical shortcomings of existing AI systems in sextortion prevention and assesses the capacity of blockchain operations to mitigate these limitations. It then develops CF-BIAI-SXT, a framework operationalized through BPMN-modeled components and structured into a three-layer implementation strategy composed of technical enablement, governance alignment, and continuous oversight. The framework is then situated within real-world regulatory constraints, including GDPR and the EU AI Act. This position paper concludes that a resilient society needs ethical, privacy-first, and socially resilient digital infrastructures, and integrating two core technologies, such as AI and blockchain, creates a viable pathway towards this desideratum. Mitigating high-risk environments, such as sextortion, may be a fundamental first step in this pathway, with the potential expansion to other forms of online threats. Full article
(This article belongs to the Special Issue AI and Blockchain: Synergies, Challenges, and Innovations)
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Review

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46 pages, 599 KB  
Review
A Review on Blockchain Sharding for Improving Scalability
by Mahran Morsidi, Sharul Tajuddin, S. H. Shah Newaz, Ravi Kumar Patchmuthu and Gyu Myoung Lee
Future Internet 2025, 17(10), 481; https://doi.org/10.3390/fi17100481 - 21 Oct 2025
Abstract
Blockchain technology, originally designed as a secure and immutable ledger, has expanded its applications across various domains. However, its scalability remains a fundamental bottleneck, limiting throughput, specifically Transactions Per Second (TPS) and increasing confirmation latency. Among the many proposed solutions, sharding has emerged [...] Read more.
Blockchain technology, originally designed as a secure and immutable ledger, has expanded its applications across various domains. However, its scalability remains a fundamental bottleneck, limiting throughput, specifically Transactions Per Second (TPS) and increasing confirmation latency. Among the many proposed solutions, sharding has emerged as a promising Layer 1 approach by partitioning blockchain networks into smaller, parallelized components, significantly enhancing processing efficiency while maintaining decentralization and security. In this paper, we have conducted a systematic literature review, resulting in a comprehensive review of sharding. We provide a detailed comparative analysis of various sharding approaches and emerging AI-assisted sharding approaches, assessing their effectiveness in improving TPS and reducing latency. Notably, our review is the first to incorporate and examine the standardization efforts of the ITU-T and ETSI, with a particular focus on activities related to blockchain sharding. Integrating these standardization activities allows us to bridge the gap between academic research and practical standardization in blockchain sharding, thereby enhancing the relevance and applicability of our review. Additionally, we highlight the existing research gaps, discuss critical challenges such as security risks and inter-shard communication inefficiencies, and provide insightful future research directions. Our work serves as a foundational reference for researchers and practitioners aiming to optimize blockchain scalability through sharding, contributing to the development of more efficient, secure, and high-performance decentralized networks. Our comparative synthesis further highlights that while Bitcoin and Ethereum remain limited to 7–15 TPS with long confirmation delays, sharding-based systems such as Elastico and OmniLedger have reported significant throughput improvements, demonstrating sharding’s clear advantage over traditional Layer 1 enhancements. In contrast to other state-of-the-art scalability techniques such as block size modification, consensus optimization, and DAG-based architectures, sharding consistently achieves higher transaction throughput and lower latency, indicating its position as one of the most effective Layer 1 solutions for improving blockchain scalability. Full article
(This article belongs to the Special Issue AI and Blockchain: Synergies, Challenges, and Innovations)
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