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Electronics
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New Advances in Distributed Computing and Its Applications
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New Advances in Distributed Computing and Its Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 6270

Special Issue Editors

Dr. Zhiwei Xu

E-Mail Website
Guest Editor
1. Haihe Laboratory of Information Technology Application Innovation, Tianjin 300350, China
2. Institute of Computing, Institute of Computing Technology Chinese Academy of Sciences, Beijing 53035, China
Interests: artificial intelligence; big data; edge computing; internet of things; computer network security
Special Issues, Collections and Topics in MDPI journals
Dr. Jianer Zhou

E-Mail Website
Guest Editor
School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China
Interests: network performance; future Internet architecture; internet measurement
Dr. Xueshuo Xie

E-Mail Website
Guest Editor
Haihe Laboratory of Information Technology Application Innovation, Tianjin 300350, China
Interests: IoT security; data-driven anomaly detection; blockchain
Dr. Zhao Huang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: hardware security; hardware Trojan detection; physically unclonable function (PUF); secure architecture; bus security for System-on-Chips (SoCs)

Special Issue Information

Dear Colleagues,

Distributed computing, especially for computing functions in the network, is beneficial for designing systems in a way that allows for joint optimization of computing and networking resources by aiming for tighter integration of computing and networking. New computing paradigms, e.g., container-based microservices, enable scalable computility and dynamic computing performance and thus improve computing performance and resilience significantly, especially in the presence of dynamic, unpredictable workload changes. On the other hand, with the proliferation of artificial intelligence and the rise of emerging applications, the potential of distributed computing has now been released. The advanced distributed computing frameworks provide an efficient and robust way to facilitate large AI models and deploy them or their components on either data center or edge computing environments. This presents exciting opportunities for innovation in areas such as autonomous driving, smart healthcare, and other intelligent applications.

The motivation of this Special Issue is to discover and promote the current advancements, techniques, innovation and real-world solutions of distributed computing and its applications. It aims to gather a comprehensive range of both quantitative and qualitative research contributions from a diverse array of individual, academic, organizational, and industry practitioners in the evolving field of distributed computing solutions. By exploring the latest advances in distributed computing, this Special Issue seeks to provide valuable insights and innovative approaches to tackle the challenges posed by this rapidly expanding domain.

Topics of interest for the Special Issue include, but are not limited to, the following:

  1. Advanced distributed computing architectures, such as edge computing, container-based microservices, and blockchain;
  2. New optimizations of distributed computing in big data processing and artificial intelligence;
  3. Innovations and challenges in the synergy between edge computing and cloud computing for intelligent applications;
  4. Innovative applications and optimizations of distributed systems in blockchain technology;
  5. Applications of distributed computing in domains like autonomous driving, smart healthcare, smart cities, and intelligent transportation;
  6. Research on security, privacy, and trust mechanisms in distributed computing;
  7. Other technologies and applications advocating distributed computing.

Dr. Zhiwei Xu
Dr. Jianer Zhou
Dr. Xueshuo Xie
Dr. Zhao Huang
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. Electronics 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

  • edge computing
  • big data processing
  • artificial intelligence
  • cloud computing
  • blockchain technology

Published Papers (7 papers)

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Research

18 pages, 500 KiB  
Article
Lightweight Privacy Protection via Adversarial Sample
by Guangxu Xie, Gaopan Hou, Qingqi Pei and Haibo Huang
Electronics 2024, 13(7), 1230; https://doi.org/10.3390/electronics13071230 - 26 Mar 2024
Viewed by 322
Abstract
Adversarial sample-based privacy protection has its own advantages compared to traditional privacy protections. Previous adversarial sample privacy protections have mostly been centralized or have not considered the issue of hardware device limitations when conducting privacy protection, especially on the user’s local device. This [...] Read more.
Adversarial sample-based privacy protection has its own advantages compared to traditional privacy protections. Previous adversarial sample privacy protections have mostly been centralized or have not considered the issue of hardware device limitations when conducting privacy protection, especially on the user’s local device. This work attempts to reduce the requirements of adversarial sample privacy protections on devices, making the privacy protection more locally friendly. Adversarial sample-based privacy protections rely on deep learning models, which generally have a large number of parameters, posing challenges for deployment. Fortunately, the model structural pruning technique has been proposed, which can be employed to reduce the parameter count of deep learning models. Based on the model pruning technique Depgraph and existing adversarial sample privacy protections AttriGuard and MemGuard, we design two structural pruning-based adversarial sample privacy protections, in which the user obtains the perturbed data through the pruned deep learning model. Extensive experiments are conducted on four datasets, and the results demonstrate the effectiveness of our adversarial sample privacy protection based on structural pruning. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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15 pages, 567 KiB  
Article
Oversea Cross-Lingual Summarization Service in Multilanguage Pre-Trained Model through Knowledge Distillation
by Xiwei Yang, Jing Yun, Bofei Zheng, Limin Liu and Qi Ban
Electronics 2023, 12(24), 5001; https://doi.org/10.3390/electronics12245001 - 14 Dec 2023
Viewed by 554
Abstract
Cross-lingual text summarization is a highly desired service for overseas report editing tasks and is formulated in a distributed application to facilitate the cooperation of editors. The multilanguage pre-trained language model (MPLM) can generate high-quality cross-lingual text summaries with simple fine-tuning. However, the [...] Read more.
Cross-lingual text summarization is a highly desired service for overseas report editing tasks and is formulated in a distributed application to facilitate the cooperation of editors. The multilanguage pre-trained language model (MPLM) can generate high-quality cross-lingual text summaries with simple fine-tuning. However, the MPLM does not adapt to complex variations, like the word order and tense in different languages. When the model performs on these languages with separate syntactic structures and vocabulary morphologies, it will lead to the low-level quality of the cross-lingual summary. The matter worsens when the cross-lingual summarization datasets are low-resource. We use a knowledge distillation framework for the cross-lingual summarization task to address the above issues. By learning the monolingual teacher model, the cross-lingual student model can effectively capture the differences between languages. Since the teacher and student models generate summaries in two languages, their representations lie on different vector spaces. In order to construct representation relationships across languages, we further propose a similarity metric, which is based on bidirectional semantic alignment, to map different language representations to the same space. In order to improve the quality of cross-lingual summaries further, we use contrastive learning to make the student model focus on the differentials among languages. Contrastive learning can enhance the ability of the similarity metric for bidirectional semantic alignment. Our experiments show that our approach is competitive in low-resource scenarios on cross-language summarization datasets in pairs of distant languages. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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20 pages, 631 KiB  
Article
Throughput Optimization for Blockchain System with Dynamic Sharding
by Chuyi Liu, Jianxiong Wan, Leixiao Li and Bingbing Yao
Electronics 2023, 12(24), 4915; https://doi.org/10.3390/electronics12244915 - 06 Dec 2023
Viewed by 889
Abstract
Sharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as [...] Read more.
Sharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL)-based methods, where the action space must be discretized to increase processability. However, the discretization degrades the quality of the solution since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and provide dynamic sharding strategies based on them. The Branching Dueling Q-Network Blockchain Sharding (BDQBS) algorithm is designed for discrete action spaces. Compared with traditional DRL algorithms, the BDQBS overcomes the drawbacks of high action space dimensions and difficulty in training neural networks. And it improves the performance of the blockchain system by 1.25 times. We also propose a sharding control algorithm based on the Parameterized Deep Q-Networks (P-DQN) algorithm, i.e., the Parameterized Deep Q-Networks Blockchain Sharding (P-DQNBS) algorithm, to efficiently handle the discrete–continuous hybrid action space without the scalability issues. Also, the method can effectively improve the TPS by up to 28%. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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17 pages, 14394 KiB  
Article
An Efficient Path Planning Method for the Unmanned Aerial Vehicle in Highway Inspection Scenarios
by Yuanlong Li, Shang Gao, Xuewen Liu, Peiliang Zuo and Haoliang Li
Electronics 2023, 12(20), 4200; https://doi.org/10.3390/electronics12204200 - 10 Oct 2023
Viewed by 951
Abstract
Unmanned aerial vehicles (UAVs) have received widespread attention due to their flexible deployment characteristics. Automated airports equipped with UAVs are expected to become important equipment for improving quality and reducing costs in many inspection scenarios. This paper focuses on the automated inspection business [...] Read more.
Unmanned aerial vehicles (UAVs) have received widespread attention due to their flexible deployment characteristics. Automated airports equipped with UAVs are expected to become important equipment for improving quality and reducing costs in many inspection scenarios. This paper focuses on the automated inspection business of UAVs dispatched by automated airports in highway scenarios. On the basis of considering the shape of highway curves, inspection targets, and the energy consumption characteristics of UAVs, planning the flight parameters of UAVs is of great significance for ensuring the effectiveness of the inspection process. This paper first sets the inspection path points for the UAV based on highway curves, and then proposes an efficient heuristic method for the nonlinear non-convex parameter optimization problem, through which the parameters of the UAV’s inspection altitude, hovering altitude, and flight speed are planned. Simulation and analysis show that the proposed method possesses good parameter planning efficiency. By combining several existing trajectory planning methods, e.g., the traversal method, the deep Q-network based method, and the genetic method, it can be concluded that the proposed method in this paper has better overall planning performance including planning efficiency and inspection effectiveness. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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18 pages, 2491 KiB  
Article
Access Control Strategy for the Internet of Vehicles Based on Blockchain and Edge Computing
by Leixiao Li, Jianxiong Wan and Chuyi Liu
Electronics 2023, 12(19), 4057; https://doi.org/10.3390/electronics12194057 - 27 Sep 2023
Viewed by 708
Abstract
Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called [...] Read more.
Data stored in the Internet of Vehicles (IoV) face problems with ease of tampering, easy disclosure and single access control. Based on this problem, we propose an access control scheme for the IoV based on blockchain, trust values and weighted attribute-based encryption, called the Blockchain Trust and Weighted Attribute-Based Access Control Strategy (BTWACS). First, we utilize both local and global blockchains to jointly maintain the generation, verification and storage of blocks, achieving distributed data storage and ensuring that data cannot arbitrarily be tampered with. Local blockchain mainly uses Road Side Unit (RSU) technology to calculate trust values, while global blockchain is mainly responsible for data storage and access policy selection. Secondly, we design a blockchain-based trust evaluation scheme called Blockchain-Based Trust Evaluation (BBTE). In this evaluation scheme, the trust value of the vehicle node is based on four factors: initial trust, historical experience trust, recommendation trust and RSU observation trust. CRITIC is used to determine the optimal weights of four factors to obtain the trust value. Then, we use the Network Simulator version 3 (NS3) to verify the security and accuracy of BBTE, improving the recognition accuracy and detection rate of malicious vehicle nodes. Finally, by mining the association relationships between attribute permissions among various roles, we construct a hierarchical access control strategy based on weight and trust, and further optimize the access strategy through pruning techniques. The experiment results indicate that this scheme can effectively respond to gray hole attacks, defamation attacks and collusion attacks from other vehicle nodes. This method can effectively reduce the computing and transmission costs of vehicles and meet the access requirements of multiple entities and roles in the IoV. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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27 pages, 2981 KiB  
Article
Analyzing Miners’ Dynamic Equilibrium in Blockchain Networks under DDoS Attacks
by Xiao Liu, Zhao Huang, Quan Wang, Xiaohong Jiang, Yin Chen and Bo Wan
Electronics 2023, 12(18), 3903; https://doi.org/10.3390/electronics12183903 - 15 Sep 2023
Cited by 1 | Viewed by 864
Abstract
Proof of work (PoW) is one of the most widely used consensus algorithms in blockchain networks. It mainly uses the competition between mining nodes to obtain block rewards. However, this competition for computational power will allow malicious nodes to obtain illegal profits, bringing [...] Read more.
Proof of work (PoW) is one of the most widely used consensus algorithms in blockchain networks. It mainly uses the competition between mining nodes to obtain block rewards. However, this competition for computational power will allow malicious nodes to obtain illegal profits, bringing potential security threats to blockchain systems. A distributed denial of service (DDoS) attack is a major threat to the PoW algorithm. It utilizes multiple nodes in the blockchain network to attack honest miners to obtain illegal rewards. To solve this problem, academia has proposed a DDoS attack detection mechanism based on reinforcement learning methods and static game modeling methods based on mining pools. However, these methods cannot effectively make miners choose the strategy with the best profit over time when facing DDoS attacks. Therefore, this paper proposes a dynamic evolutionary game model for miners facing DDoS attacks under blockchain networks to solve the above problems for the first time. We address the model by replicating the dynamic equation to obtain a stable solution. According to the theorem of the Lyapunov method, we also obtain the only stable strategy for miners facing DDoS attacks. The experimental results show that compared with the static method, the dynamic method can affect game playing and game evolution over time. Moreover, miners’ strategy to face DDoS attacks gradually shifts from honest mining to launching DDoS attacks against each other as the blockchain network improves. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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18 pages, 598 KiB  
Article
Hierarchical Decentralized Federated Learning Framework with Adaptive Clustering: Bloom-Filter-Based Companions Choice for Learning Non-IID Data in IoV
by Siyuan Liu, Zhiqiang Liu, Zhiwei Xu, Wenjing Liu and Jie Tian
Electronics 2023, 12(18), 3811; https://doi.org/10.3390/electronics12183811 - 08 Sep 2023
Cited by 2 | Viewed by 959
Abstract
The accelerating progress of the Internet of Vehicles (IoV) has put forward a higher demand for distributed model training and data sharing in vehicular networks. Traditional centralized approaches are no longer applicable in the face of drivers’ concerns about data privacy, while Decentralized [...] Read more.
The accelerating progress of the Internet of Vehicles (IoV) has put forward a higher demand for distributed model training and data sharing in vehicular networks. Traditional centralized approaches are no longer applicable in the face of drivers’ concerns about data privacy, while Decentralized Federated Learning (DFL) provides new possibilities to address this issue. However, DFL still faces challenges regarding the non-IID data of passing vehicles. To tackle this challenge, a novel DFL framework, Hierarchical Decentralized Federated Learning (H-DFL), is proposed to achieve qualified distributed training among vehicles by considering data complementarity. We include vehicles, base stations, and data center servers in this framework. Firstly, a novel vehicle-clustering paradigm is designed to group passing vehicles based on the Bloom-filter-based compact representation of data complementarity. In this way, vehicles train their models based on local data, exchange model parameters in each group, and achieve a qualified local model without the interference of imbalanced data. On a higher level, a local model trained by each group is submitted to the data center to obtain a model covering global features. Base stations maintain the local models of different groups and judge whether the local models need to be updated according to the global model. The experimental results based on real-world data demonstrate that H-DFL dose not only reduces communication latency with different participants but also addresses the challenges of non-IID data in vehicles. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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