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Applied Sciences
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Blockchain and Intelligent Networking for Smart Applications
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Blockchain and Intelligent Networking for Smart Applications

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 10604

Special Issue Editor

Prof. Dr. Zheng Chang

E-Mail Website
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

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

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

Published Papers (6 papers)

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Research

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24 pages, 1792 KiB  
Article
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
Viewed by 432
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 [...] Read more.
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  
Article
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 516
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 [...] Read more.
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  
Article
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 5 | Viewed by 2149
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 [...] Read more.
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  
Article
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 1 | Viewed by 1654
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 [...] Read more.
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  
Article
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 1181
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 [...] Read more.
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  
Review
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 3 | Viewed by 3710
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 [...] Read more.
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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