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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 December 2024) | Viewed by 29801

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Special Issue Editor

Prof. Dr. Zheng Chang

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Guest Editor

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: IoT; blockchain; resource allocation; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The future network should be able to support ubiquitous information exchange and content sharing among smart devices with little or no human intervention, which is a key enabler for various smart applications, such as smart city, smart grid, smart health and intelligent transportation systems. In this context, smart applications explore many new advancements in the development of future networks, including artificial intelligence (AI) and Blockchain technology. Blockchain technology is defined as a distributed ledger to securely store information across several systems and to enable peer-to-peer transactions by creating a trustworthy source of ‘truth’, avoiding the “intermediaries of trust”. Meanwhile, AI is considered as the key enabler for future Intelligent network evolution. Pushing AI frontiers to the edge of the network can enable future networks with self-automating operations and maintenance smart functions with limited human involvement. Consequently, a wide range of smart applications can be imagined with the development of AI and Blockchain, such as smart city, smart transportation, smart logistics, smart industry and smart agriculture.

This Special Issue in the Journal Applied Sciences, “Blockchain and Internet of Things for Smart Applications”, aims to cover recent advances in the use of Blockchain and AI technologies in the development of a number of smart applications, obtained by researchers from both academia and industry.

Prof. Dr. Zheng Chang
Guest Editor

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Keywords

blockchain
artificial intelligence
edge/cloud/fog computing
smart industry
smart city
smart applications

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Published Papers (11 papers)

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Research

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53 pages, 2559 KiB

Open AccessArticle

Multiple-Valued Logic, Vocabulary Structure, and Linked List for Data Verification in Dialog Communications of Agents

by Alexey Bykovsky

Appl. Sci. 2025, 15(5), 2427; https://doi.org/10.3390/app15052427 - 24 Feb 2025

Viewed by 355

Abstract

Distant verification of the autonomous agent’s parameters in the dialog mode is a difficult multi-parametric task if the large-scale scene of action is characterized by a large number of collaborative and rival robots. The possible scheme to realize it for mass robots is to use non-exhaustive and selective data verification, combining the polling of internal subsystems and external data storage in collaborating network agents. Selective extraction of data for such checks is proposed to involve the special ordered set of vocabularies, containing coded digital words and classifying parameters of agents, tasks, objects, and events. The structure of such vocabularies is to be combined with various versions of the linked list scheme, known in blockchain and actual for protective documenting of critical data. Multiple-valued logic is used here as the convenient method to provide autonomous navigation in a multi-parametric structure of data and verification variables. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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13 pages, 471 KiB

Open AccessArticle

An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning

by Hani Alnami, Muhammad Mohzary, Basem Assiri and Hussein Zangoti

Appl. Sci. 2025, 15(4), 1864; https://doi.org/10.3390/app15041864 - 11 Feb 2025

Viewed by 3407

Abstract

The accurate prediction of cryptocurrency prices is crucial due to the volatility and complexity of digital asset markets, which pose significant challenges to traders, investors, and researchers. This research addresses these challenges by leveraging machine learning and deep learning techniques to forecast closing prices for cryptocurrencies, focusing on Bitcoin, Ethereum, Binance Coin, and Litecoin cryptocurrency datasets. A Random Forest ensemble learning algorithm, a Gradient Boosting model, and a feedforward neural network were implemented to handle the complexities in cryptocurrency data. A Z-Score-based anomaly detection framework was integrated to classify closing prices as normal or abnormal, aiding in identifying significant market events. Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. The deep learning model indicates strong generalization capabilities, suggesting potential advantages on more complex datasets. These findings highlight the importance of combining advanced machine learning techniques and cryptocurrencies to develop a robust framework for cryptocurrency forecasting and anomaly detection. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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26 pages, 4473 KiB

Open AccessArticle

The Blockchain Trilemma: A Formal Proof of the Inherent Trade-Offs Among Decentralization, Security, and Scalability

by Souhail Mssassi and Anas Abou El Kalam

Appl. Sci. 2025, 15(1), 19; https://doi.org/10.3390/app15010019 - 24 Dec 2024

Cited by 1 | Viewed by 2760

Abstract

Blockchain technology has emerged as a transformative force with the potential to disrupt traditional industries by providing decentralized, secure, and transparent systems. However, blockchain systems face a fundamental challenge known as the Blockchain Trilemma, which posits that a blockchain cannot simultaneously achieve optimal levels of decentralization, security, and scalability. This paper addresses the Blockchain Trilemma through rigorous mathematical proof, demonstrating that achieving maximum levels of all three properties concurrently leads to inherent contradictions. By formalizing the definitions of decentralization, security, and scalability, we provide a foundational framework to understand these trade-offs. Our analysis includes a proof by contradiction and computational complexity analysis. This work aims to deepen the understanding of the fundamental limitations of blockchain technology, offering insights for future innovations and helping navigate the inevitable trade-offs. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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16 pages, 1639 KiB

Open AccessArticle

Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm

by Hyunjun Kim, Wonwoong Kim, Yeajun Kang, Hyunji Kim and Hwajeong Seo

Appl. Sci. 2024, 14(18), 8394; https://doi.org/10.3390/app14188394 - 18 Sep 2024

Cited by 1 | Viewed by 2622

Abstract

The advancements in quantum computing and the potential for polynomial-time solutions to traditional public key cryptography (i.e., Rivest–Shamir–Adleman (RSA) and elliptic-curve cryptography (ECC)) using Shor’s algorithm pose a serious threat to the security of pre-quantum blockchain technologies. This paper proposes an efficient quantum-safe blockchain that incorporates new quantum-safe consensus algorithms. We integrate post-quantum signature schemes into the blockchain’s transaction signing and verification processes to enhance resistance against quantum attacks. Specifically, we employ the Falcon signature scheme, which was selected during the NIST post-quantum cryptography (PQC) standardization process. Although the integration of the post-quantum signature scheme results in a reduction in the blockchain’s transactions per second (TPSs), we introduce efficient approaches to mitigate this performance degradation. Our proposed post-quantum delegated proof of luck (PQ-DPoL) combines a proof of luck (PoL) mechanism with a delegated approach, ensuring quantum resistance, energy efficiency, and fairness in block generation. Experimental results demonstrate that while post-quantum cryptographic algorithms like Falcon introduce larger signature sizes and slower processing times, the PQ-DPoL algorithm effectively balances security and performance, providing a viable solution for secure blockchain operations in a post-quantum era. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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13 pages, 1338 KiB

Open AccessArticle

Leveraging Blockchain Usage to Enhance Slag Exchange

by Aitor Gómez-Goiri, Ivan Gutierrez-Aguero and David Garcia-Estevez

Appl. Sci. 2024, 14(14), 6243; https://doi.org/10.3390/app14146243 - 18 Jul 2024

Cited by 1 | Viewed by 1139

Abstract

The slag generated as a by-product of the steelmaking process can be used to manufacture cement, reducing the generated waste and contributing to the circular economy. Currently, steelmaking companies promote long-term bilateral deals with one or few cement companies where the price is fixed, and the slag is a treated as commodity. We propose a new solution, which promotes slag reuse through its differentiation with a composition-based grouping and an auction. This process is carried out in a blockchain network, which increases trust in the system, provides guarantees about the slag composition to cement companies and helps external regulators to reliably extract circularity indicators. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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24 pages, 1792 KiB

Open AccessArticle

D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing

by Zhenhua Li, Ke Zhang, Yuhan Zhang, Yanyue Liu and Yi Chen

Appl. Sci. 2024, 14(12), 4989; https://doi.org/10.3390/app14124989 - 7 Jun 2024

Cited by 2 | Viewed by 1357

Abstract

The booming growth of the internet of things has brought about widespread deployment of devices and massive amounts of sensing data to be processed. Federated learning (FL)-empowered mobile edge computing, known for pushing artificial intelligence to the network edge while preserving data privacy in learning cooperation, is a promising way to unleash the potential information of the data. However, FL’s multi-server collaborative operating architecture inevitably results in communication energy consumption between edge servers, which poses great challenges to servers with constrained energy budgets, especially wireless communication servers that rely on battery power. The device-to-device (D2D) communication mode developed for FL turns high-cost and long-distance server interactions into energy-efficient proximity delivery and multi-level aggregations, effectively alleviating the server energy constraints. A few studies have been devoted to D2D-enabled FL management, but most of them have neglected to investigate server computing power for FL operation, and they have all ignored the impact of dataset characteristics on model training, thus failing to fully exploit the data processing capabilities of energy-constrained edge servers. To fill this gap, in this paper we propose a D2D-assisted FL mechanism for energy-constrained edge computing, which jointly incorporates computing power allocation and dataset correlation into FL scheduling. In view of the variable impacts of computational power on model accuracy at different training stages, we design a partite graph-based FL scheme with adaptive D2D pairing and aperiodic variable local iterations of heterogeneous edge servers. Moreover, we leverage graph learning to exploit the performance gain of the dataset correlation between the edge servers in the model aggregation process, and we propose a graph-and-deep reinforcement learning-based D2D server pairing algorithm, which effectively reduces FL model error. The numerical results demonstrate that our designed FL schemes have great advantages in improving FL training accuracy under edge servers’ energy constraints. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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24 pages, 1082 KiB

Open AccessArticle

Improving Supply Chain Management Processes Using Smart Contracts in the Ethereum Network Written in Solidity

by Eren Yigit and Tamer Dag

Appl. Sci. 2024, 14(11), 4738; https://doi.org/10.3390/app14114738 - 30 May 2024

Viewed by 3648

Abstract

This paper investigates the potential of integrating supply chain management with blockchain technology, specifically by implementing smart contracts on the Ethereum network using Solidity. The paper explores supply chain management concepts, blockchain, distributed ledger technology, and smart contracts in the context of their integration into supply chains to increase traceability, transparency, and accountability with faster processing times. After investigating these technologies’ applications and potential use cases, a framework for smart contract implementation for supply chain management is constructed. Potential data models and functions of a smart contract implementation improving supply chain management processes are discussed. After constructing a framework, the effects of the proposed system on supply chain processes are explained. The proposed framework increases the reliability of the supply chain history due to the usage of DLT (distributed ledger technology). It utilizes smart contracts to increase the manageability and traceability of the supply chain. The proposed framework also eliminates the SPoF (Single Point of Failure) vulnerabilities and external alteration of the transactional data. However, due to the ever-changing and variable nature of the supply chains, the proposed architecture might not be a one-size-fits-all solution, and tailor-made solutions might be necessary for different supply chain management implementations. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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32 pages, 9377 KiB

Open AccessArticle

Toward Patient-Centric Healthcare Systems: Key Requirements and Framework for Personal Health Records Based on Blockchain Technology

by Ohud Aldamaeen, Waleed Rashideh and Waeal J. Obidallah

Appl. Sci. 2023, 13(13), 7697; https://doi.org/10.3390/app13137697 - 29 Jun 2023

Cited by 11 | Viewed by 3206

Abstract

Healthcare data are considered sensitive and confidential, and storing these sensitive data in traditional (i.e., centralized) databases may expose risks, such as penetration or data leaks. Furthermore, patients may have incomplete health records since they visit various healthcare centers and leave their data scattered in different places. One solution to resolve these problems and permit patients to own their records is a decentralized personal health record (PHR); this can be achieved through decentralization and distribution systems, which are fundamental attributes of blockchain technology. Additionally, the requirements for this solution should be identified to provide practical solutions for stakeholders. This study aims to identify the key requirements for PHRs. A design science methodology was utilized to meet the study objectives, and thirteen healthcare experts were interviewed to elicit the requirements and the previous studies. Thirty-three requirements are defined, and based on these, high- and low-level architectures are developed and explained. The result illustrates that the developed solution-based Hyperledger Fabric framework is a promising method for the achievement of PHRs that guarantee security aspects, such as integrity, confidentiality, privacy, traceability, and access control. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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23 pages, 6802 KiB

Open AccessArticle

Non-Face-to-Face P2P (Peer-to-Peer) Real-Time Token Payment Blockchain System

by Hyug-Jun Ko, Seong-Soo Han and Chang-Sung Jeong

Appl. Sci. 2023, 13(13), 7364; https://doi.org/10.3390/app13137364 - 21 Jun 2023

Cited by 3 | Viewed by 2382

Abstract

With the increase in intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals’ personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas-charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smartphones or application platforms. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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21 pages, 5808 KiB

Open AccessArticle

Dynamic Pathway Selection Mechanisms of Brain Networks

by Yanhui Chen, Yun Hu, Jinhui Liu, Yu Wang and Aiting Li

Appl. Sci. 2023, 13(1), 296; https://doi.org/10.3390/app13010296 - 26 Dec 2022

Cited by 1 | Viewed by 1511

Abstract

Based on the dynamic reorganization mechanism of brain science and the fact that synaptic adaptability is affected by synaptic type, synaptic number and ion concentration, a bionic dynamic synaptic model is proposed and applied to a motif model and brain-like network model. By extracting the phase synchronization characteristics of the neural signals of node pairs in time sequence, and then deeply studying the regulation and control effect of synchronous discharge activities on effective links under the action of stimulating information, the path selection strategy is designed with the goal of maximizing the information transmission capacity between nodes. Four indicators are proposed: (1) pathway-synchronization-facilitation; (2) pathway-activation; (3) pathway-phase-selectivity; (4) pathway-switching-selectivity, which are used as the main basis for path selection in the network. The results show that the in-phase and anti-phase transition of neuron nodes under the action of time delay is an important way to form an effective link, and, in addition to the influence of synaptic strength and the number of central nodes on synchronization characteristics, the phase information carried by the stimulus signal also regulates the path selection. Furthermore, the paths between the pairs of stimulus nodes in the network have different phase preferences. In the brain-like network with twenty nodes, it is found that nearly 42% of the stimulus nodes have a strong phase preference; that is, the path can be selected and switched through the phase information carried by the information flow, and then the path with better representation information can be found. It also provides a new idea for how brain-like intelligences might better represent information. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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Review

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28 pages, 726 KiB

Open AccessReview

Challenges, Issues, and Recommendations for Blockchain- and Cloud-Based Automotive Insurance Systems

by Abdul Mateen, Adia Khalid, Sihyung Lee and Seung Yeob Nam

Appl. Sci. 2023, 13(6), 3561; https://doi.org/10.3390/app13063561 - 10 Mar 2023

Cited by 6 | Viewed by 5529

Abstract

Despite the rapid expansion in the insurance industry, many issues remain unresolved and may require immediate action. As the insurance sector continues to evolve with the development of new technologies, it faces more challenges, especially related to data security and fraud. The fraud-prevention data and tactics presently used by insurance firms are outdated and ineffective. Additionally, insurance firms have traditionally handled the settlement of all consumer claims through lengthy manual processes. These manual processes need to be changed to provide opportunities for insurance businesses to grow. In the case of vehicles, the information obtained from an automobile data recorder can be used as evidence. Data from automated vehicles are critical because they can help the police, law enforcement agencies, and insurance companies to reconstruct the events leading up to a collision. Insurance companies require the forensic analysis of accident videos, which is a time-consuming process and involves a large amount of storage. Due to hardware limitations and associated costs, the current standalone (and often dedicated) computing infrastructures used for this purpose are quite limited. Previous research focused on simple video analysis tasks within cloud computing and blockchain technology. The requirements for a large-scale auto-insurance system are quite high and need more thorough investigation. In this paper, a review of the contribution of recent approaches to storing accidental data in cloud computing using blockchain is provided. We focused on the latest cloud and blockchain studies related to auto-insurance along with the related issues and challenges. Some useful solutions and recommendations are provided to address the identified issues and challenges in the cloud-based and blockchain-based auto-insurance sector. Full article

(This article belongs to the Special Issue Blockchain and Intelligent Networking for Smart Applications)

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