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Future Trends in Internet of Everything (IoE): Blockchain and Edge...
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Future Trends in Internet of Everything (IoE): Blockchain and Edge Computing Perspectives

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2025) | Viewed by 3734

Special Issue Editors

Dr. Yi Sun

E-Mail Website
Guest Editor
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Interests: blockchain; distributed applications
Dr. Guoyan Zhang

E-Mail Website
Guest Editor
School of Cyber Science and Technology, Shandong University, Qingdao 266237, China
Interests: cryptography analysis and design; privacy computing; blockchian

Special Issue Information

Dear Colleagues,

Blockchain technology, with its immutability and decentralization features, demonstrates strong influence and transformative power in several key areas, including finance, IoT and logistics, public administration, and healthcare. Meanwhile, edge computing integrates computational power, data storage, and diverse application services. In next-generation internet, edge computing is the complex interweaving of multiple and
varied technologies, including P2P systems, wireless networks, and visualization technologies, collectively crafting a smarter and more efficient digital ecosystem. Notably, the convergence of edge computing and blockchain technology signals groundbreaking advancements in security, privacy protection, and decentralized governance, promising to revolutionize existing network management systems and architectures. In this field of boundless possibilities, we aim to deeply integrate the distributed trust mechanisms of blockchain with the processing capabilities of edge computing. This integration aims not only to achieve a qualitative leap in data security but also to reach unprecedented levels of efficiency and reliability in data processing, collectively driving the creation of a more secure, efficient, and trustworthy future digital world.

Scope: 

  • Internet of Things security;
  • Blockchain;
  • Big data;
  • Computing security;
  • Distributed system;
  • Edge artificial intelligence.

Dr. Yi Sun
Dr. Guoyan Zhang
Guest Editors

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Keywords

  • blockchain
  • edge computing
  • data security
  • privacy protection
  • distributed systems security
  • large scale model
  • cryptography

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Published Papers (5 papers)

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Research

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15 pages, 1020 KB  
Article
On-Chain/Off-Chain Adaptive Low-Latency Network Communication Technology with High Security and Regulatory Compliance
by Yu Jin, Daming Huang and Chen Tian
Appl. Sci. 2025, 15(16), 8880; https://doi.org/10.3390/app15168880 - 12 Aug 2025
Viewed by 314
Abstract
The rapid advancement of blockchain technology has introduced a new paradigm for constructing trusted digital economic infrastructure. However, its large-scale adoption remains constrained by dual challenges: on-chain and off-chain communication efficiency and security assurance. This paper addresses the universal demands of blockchain in [...] Read more.
The rapid advancement of blockchain technology has introduced a new paradigm for constructing trusted digital economic infrastructure. However, its large-scale adoption remains constrained by dual challenges: on-chain and off-chain communication efficiency and security assurance. This paper addresses the universal demands of blockchain in complex application scenarios by proposing a low-latency, high-security, adaptive, and regulatory-compliant network communication technology bridging on-chain and off-chain systems. A hierarchical “device–edge–chain” communication architecture based on edge gateways is designed to address the critical challenge of achieving one-second on-chain processing for tens of millions of data entries. Experimental validation demonstrates that the system sustains transaction throughput at the scale of at least 10 million while consistently maintaining sub-second latency thresholds. Furthermore, implemented fault tolerance mechanisms ensure reliable operation through dynamic path switching and capacity-aware load redistribution. This architecture systematically resolves the performance–security–regulatory compliance trilemma inherent in conventional blockchain systems deployed within complex real-world environments. Full article
(This article belongs to the Special Issue Future Trends in Internet of Everything (IoE): Blockchain and Edge Computing Perspectives)
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17 pages, 362 KB  
Article
An Efficient Distributed Identity Selective Disclosure Algorithm
by Guanzheng Wang and Guoyan Zhang
Appl. Sci. 2025, 15(16), 8834; https://doi.org/10.3390/app15168834 - 11 Aug 2025
Viewed by 295
Abstract
Distributed digital identity is an emerging identity management technology aimed at achieving comprehensive interconnectivity between digital objects. However, there is still the problem of privacy leakage in distributed identities, and selective disclosure technology partially solves the privacy issue in distributed identities. Most of [...] Read more.
Distributed digital identity is an emerging identity management technology aimed at achieving comprehensive interconnectivity between digital objects. However, there is still the problem of privacy leakage in distributed identities, and selective disclosure technology partially solves the privacy issue in distributed identities. Most of the existing selective disclosure algorithms use anonymous credentials or hash functions. Anonymous credential schemes offer high security and meet the requirements of unforgeability and unlinkability, but their exponential operations result in low efficiency. The scheme based on hash functions, although more efficient, is susceptible to man-in-the-middle attacks. This article proposes an efficient selective disclosure scheme based on hash functions and implicit certificates. The attribute values are treated as leaf nodes of the Merkle tree, and the root node is placed in a verifiable credential. According to the implicit certificate algorithm process, a key pair that can use the credential is generated. During the attribute disclosure process, the user autonomously selects the attribute value to be presented and generates a verification path from the attribute to the root node. The verifier checks the Merkle tree verification path. All operations are completed within 10 ms while meeting the unforgeability requirements and resisting man-in-the-middle attacks. This article also utilizes the ZK-SNARK algorithm to hide the validation path of the Merkle tree, enhancing the security of the path during the disclosure process. The experimental results show that the selective disclosure algorithm performs well in both performance and privacy protection, with an efficiency 80% faster than that of existing schemes. This enhances the proposed scheme’s potential and value in the field of identity management; it also holds broad application prospects in fields such as the Internet of Things, finance, and others. Full article
(This article belongs to the Special Issue Future Trends in Internet of Everything (IoE): Blockchain and Edge Computing Perspectives)
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25 pages, 1107 KB  
Article
Provenance Graph-Based Deep Learning Framework for APT Detection in Edge Computing
by Tianyi Wang, Wei Tang, Yuan Su and Jiliang Li
Appl. Sci. 2025, 15(16), 8833; https://doi.org/10.3390/app15168833 - 11 Aug 2025
Viewed by 271
Abstract
Edge computing builds relevant services and applications on the edge server near the user side, which enables a faster service response. However, the lack of large-scale hardware resources leads to weak defense for edge devices. Therefore, proactive defense security mechanisms, such as Intrusion [...] Read more.
Edge computing builds relevant services and applications on the edge server near the user side, which enables a faster service response. However, the lack of large-scale hardware resources leads to weak defense for edge devices. Therefore, proactive defense security mechanisms, such as Intrusion Detection Systems (IDSs), are widely deployed in edge computing. Unfortunately, most of those IDSs lack causal analysis capabilities and still suffer the threats from Advanced Persistent Threat (APT) attacks. To effectively detect APT attacks, we propose a heterogeneous graph neural networks threat detection model based on the provenance graph. Specifically, we leverage the powerful analysis and tracking capabilities of the provenance graph to model the long-term behavior of the adversary. Moreover, we leverage the predictive power of heterogeneous graph neural networks to embed the provenance graph by a node-level and semantic-level heterogeneous mutual attention mechanism. In addition, we also propose a provenance graph reduction algorithm based on the semantic similarity of graph substructures to improve the detection efficiency and accuracy of the model, which reduces and integrates redundant information by calculating the semantic similarity between substructures. The experimental results demonstrate that the prediction accuracy of our method reaches 99.8% on the StreamSpot dataset and achieves 98.13% accuracy on the NSL-KDD dataset. Full article
(This article belongs to the Special Issue Future Trends in Internet of Everything (IoE): Blockchain and Edge Computing Perspectives)
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26 pages, 2806 KB  
Article
The YouGovern Secure Blockchain-Based Self-Sovereign Identity (SSI) Management and Access Control
by Nikos Papatheodorou, George Hatzivasilis and Nikos Papadakis
Appl. Sci. 2025, 15(12), 6437; https://doi.org/10.3390/app15126437 - 7 Jun 2025
Cited by 2 | Viewed by 1165
Abstract
Self-sovereign identity (SSI) is an emerging model for digital identity management that empowers individuals to control their credentials without reliance on centralized authorities. This work presents YouGovern, a blockchain-based SSI system deployed on Binance Smart Chain (BSC) and compliant with W3C Decentralized Identifier [...] Read more.
Self-sovereign identity (SSI) is an emerging model for digital identity management that empowers individuals to control their credentials without reliance on centralized authorities. This work presents YouGovern, a blockchain-based SSI system deployed on Binance Smart Chain (BSC) and compliant with W3C Decentralized Identifier (DID) standards. The architecture includes smart contracts for access control, decentralized storage using the Inter Planetary File System (IPFS), and long-term persistence via Web3.Storage. YouGovern enables users to register, share, and revoke identities while preserving privacy and auditability. The system supports role-based permissions, verifiable claims, and cryptographic key rotation. Performance was evaluated using Ganache and Hardhat under controlled stress tests, measuring transaction latency, throughput, and gas efficiency. Results indicate an average DID registration latency of 0.94 s and a peak throughput of 12.5 transactions per second. Compared to existing SSI systems like Sovrin and uPort, YouGovern offers improved revocation handling, lower operational costs, and seamless integration with decentralized storage. The system is designed for portability and real-world deployment in academic, municipal, or governmental settings. Full article
(This article belongs to the Special Issue Future Trends in Internet of Everything (IoE): Blockchain and Edge Computing Perspectives)
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Review

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46 pages, 3093 KB  
Review
Security and Privacy in the Internet of Everything (IoE): A Review on Blockchain, Edge Computing, AI, and Quantum-Resilient Solutions
by Haluk Eren, Özgür Karaduman and Muharrem Tuncay Gençoğlu
Appl. Sci. 2025, 15(15), 8704; https://doi.org/10.3390/app15158704 - 6 Aug 2025
Viewed by 1009
Abstract
The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient [...] Read more.
The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security. Full article
(This article belongs to the Special Issue Future Trends in Internet of Everything (IoE): Blockchain and Edge Computing Perspectives)
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