Recent Advances and Challenges in IoT, Cloud and Edge Coexistence

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

Deadline for manuscript submissions: 1 October 2024 | Viewed by 12112

Special Issue Editors


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Guest Editor
Department of Information and Communication Systems Engineering, University of the Aegean, 83100 Samos, Greece
Interests: novel internet architectures and services; cloud computing & networking; energy & context aware next generation networks and services; management aspects of mobile and wireless networks; ubiquitous and pervasive computing and end-to-end quality of service provisioning in heterogeneous networks environment

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Guest Editor
Eclipse Foundation, 31000 Toulouse, France
Interests: open source; community building; ecosystems

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Guest Editor
Atos Research & Innovation, 28037 Madrid, Spain
Interests: edge; cloud; DevOps

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Guest Editor
Netcompany-Intrasoft S.A., 19 5 KM Markopoulou Ave., 19002 Peania, Attika, Greece
Interests: Internet of Things; artificial intelligence; edge computing

Special Issue Information

Dear Colleagues,

The coexistence of the IoT, Cloud and Edge (ICE) provides a stronghold for innovation that spans across all sectors of vertical industries, research, and development ecosystems towards the ever-increasing volume of data and the associated burst of business cases.

The ICE continuum is driven to the next generation via intelligence, scalability, openness, risk awareness, security, greenness and monetization, enabling massive timely development, deployment and migration of devices and services in diverse operational environments, effective management of risks and vulnerabilities and cross-sector exploitation of dynamic data sets.

In this Special Issue, the aim is to publish high-quality articles including reviews and position papers that address various challenges in the use of these technologies (IoT, cloud and edge computing) towards their technical and business fusion, realizing a computing continuum enhanced by AI and openness.

The topics of interest include but are not limited to:

  • Meta-operating systems;
  • Security, privacy and trust;
  • Open source enabling platforms and tools;
  • Artificial intelligence;
  • Data and identity management;
  • Vertical industries;
  • Orchestration and management technologies;
  • Business modeling and commercialization;
  • Sustainable Development Goals;
  • Digital twins;
  • Blockchain technologies.

Dr. Charalabos Skianis
Dr. Philippe Krief
Dr. Enric Pages Montanera
Dr. John Soldatos
Guest Editors

Manuscript Submission Information

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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

  • Internet of Things
  • cloud
  • edge
  • open source
  • artificial intelligence
  • security
  • data management
  • sustainability

Published Papers (5 papers)

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Research

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22 pages, 1083 KiB  
Article
FLRAM: Robust Aggregation Technique for Defense against Byzantine Poisoning Attacks in Federated Learning
by Haitian Chen, Xuebin Chen, Lulu Peng and Ruikui Ma
Electronics 2023, 12(21), 4463; https://doi.org/10.3390/electronics12214463 - 30 Oct 2023
Cited by 1 | Viewed by 1261
Abstract
In response to the susceptibility of federated learning, which is based on a distributed training structure, to byzantine poisoning attacks from malicious clients, resulting in issues such as slowed or disrupted model convergence and reduced model accuracy, we propose a robust aggregation technique [...] Read more.
In response to the susceptibility of federated learning, which is based on a distributed training structure, to byzantine poisoning attacks from malicious clients, resulting in issues such as slowed or disrupted model convergence and reduced model accuracy, we propose a robust aggregation technique for defending against byzantine poisoning attacks in federated learning, known as FLRAM. First, we employ isolation forest and an improved density-based clustering algorithm to detect anomalies in the amplitudes and symbols of client local gradients, effectively filtering out gradients with large magnitude and angular deviation variations. Subsequently, we construct a credibility matrix based on the filtered subset of gradients to evaluate the trustworthiness of each local gradient. Using this credibility score, we further select gradients with higher trustworthiness. Finally, we aggregate the filtered gradients to obtain the global gradient, which is then used to update the global model. The experimental findings show that our proposed approach achieves strong defense performance without compromising FedAvg accuracy. Furthermore, it exhibits superior robustness compared to existing solutions. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence)
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18 pages, 3232 KiB  
Article
Vulnerability Identification and Assessment for Critical Infrastructures in the Energy Sector
by Nikolaos Nikolaou, Andreas Papadakis, Konstantinos Psychogyios and Theodore Zahariadis
Electronics 2023, 12(14), 3185; https://doi.org/10.3390/electronics12143185 - 22 Jul 2023
Cited by 1 | Viewed by 1653
Abstract
Vulnerability identification and assessment is a key process in risk management. While enumerations of vulnerabilities are available, it is challenging to identify vulnerability sets focused on the profiles and roles of specific organizations. To this end, we have employed systematized knowledge and relevant [...] Read more.
Vulnerability identification and assessment is a key process in risk management. While enumerations of vulnerabilities are available, it is challenging to identify vulnerability sets focused on the profiles and roles of specific organizations. To this end, we have employed systematized knowledge and relevant standards (including National Electric Sector Cybersecurity Organization Resource (NESCOR), ISO/IEC 27005:2018 and National Vulnerability Database (NVD)) to identify a set of 250 vulnerabilities for operators of energy-related critical infrastructures. We have elaborated a “double-mapping” scheme to associate (arbitrarily) categorized assets, with the pool of identified Physical, Cyber and Human/Organizational vulnerabilities. We have designed and implemented an extensible vulnerability identification and assessment framework, allowing historized assessments, based on the CVSS (Common Vulnerability Scoring System) scoring mechanism. This framework has been extended to allow modelling of the vulnerabilities and assessments using the Structured Threat Information eXpression (STIX) JSON format, as Cyber Threat Intelligence (CTI) information, to facilitate information sharing between Electrical Power and Energy Systems (EPES) and to promote collaboration and interoperability scenarios. Vulnerability assessments from the initial analysis of the project in the context of Research and Technology Development (RTD) projects have been statistically processed, offering insights in terms of the assessment’s importance and distribution. The assessments have also been transformed into a dynamic dataset processed to identify and quantify correlation and start the discussion on the interpretation of the way assessments are performed. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence)
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20 pages, 3140 KiB  
Article
Better Safe Than Sorry: Constructing Byzantine-Robust Federated Learning with Synthesized Trust
by Gangchao Geng, Tianyang Cai and Zheng Yang
Electronics 2023, 12(13), 2926; https://doi.org/10.3390/electronics12132926 - 3 Jul 2023
Cited by 2 | Viewed by 1088
Abstract
Byzantine-robust federated learning empowers the central server to acquire high-end global models amidst a restrictive set of malicious clients. The general idea of existing learning methods requires the central server to statistically analyze all local parameter (gradient or weight) updates, and to delete [...] Read more.
Byzantine-robust federated learning empowers the central server to acquire high-end global models amidst a restrictive set of malicious clients. The general idea of existing learning methods requires the central server to statistically analyze all local parameter (gradient or weight) updates, and to delete suspicious ones. The drawback of these approaches is that they lack a root of trust that would allow us to identify which local parameter updates are suspicious, which means that malicious clients can still disrupt the global model. The machine learning community has recently proposed a new method, FLTrust (NDSS’2021), where the server achieves robust aggregation by using a tiny, uncontaminated dataset (denoted as the root dataset) to generate the root of trust; however, the global model’s accuracy will significantly decline if the root dataset greatly deviates from the client’s dataset. To address the above problems, we propose FLEST: a Federated LEarning with Synthesized Trust method. Our method considers that trust and anomaly detection methods can complementarily solve their respective problems; therefore, we designed a new robust aggregation rule with synthesized trust scores (STS). Specifically, we propose the trust synthesizing mechanism, which can aggregate trust scores (TS) and confidence scores (CS) into STS through a dynamic trust ratio γ, and we use STS as the weight for aggregating the local parameter updates. Our experimental results demonstrated that FLEST is capable of resisting existing attacks, even when the root dataset distribution significantly differs from the total dataset distribution: for example, the global model trained by FLEST is 41% more accurate than FLTrust for adaptive attacks using the mnist-0.5 dataset with the bias probability set to 0.8. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence)
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17 pages, 2438 KiB  
Article
GAN-Driven Data Poisoning Attacks and Their Mitigation in Federated Learning Systems
by Konstantinos Psychogyios, Terpsichori-Helen Velivassaki, Stavroula Bourou, Artemis Voulkidis, Dimitrios Skias and Theodore Zahariadis
Electronics 2023, 12(8), 1805; https://doi.org/10.3390/electronics12081805 - 11 Apr 2023
Cited by 2 | Viewed by 2995
Abstract
Federated learning (FL) is an emerging machine learning technique where machine learning models are trained in a decentralized manner. The main advantage of this approach is the data privacy it provides because the data are not processed in a centralized device. Moreover, the [...] Read more.
Federated learning (FL) is an emerging machine learning technique where machine learning models are trained in a decentralized manner. The main advantage of this approach is the data privacy it provides because the data are not processed in a centralized device. Moreover, the local client models are aggregated on a server, resulting in a global model that has accumulated knowledge from all the different clients. This approach, however, is vulnerable to attacks because clients can be malicious or malicious actors may interfere within the network. In the first case, these types of attacks may refer to data or model poisoning attacks where the data or model parameters, respectively, may be altered. In this paper, we investigate the data poisoning attacks and, more specifically, the label-flipping case within a federated learning system. For an image classification task, we introduce two variants of data poisoning attacks, namely model degradation and targeted label attacks. These attacks are based on synthetic images generated by a generative adversarial network (GAN). This network is trained jointly by the malicious clients using a concatenated malicious dataset. Due to dataset sample limitations, the architecture and learning procedure of the GAN are adjusted accordingly. Through the experiments, we demonstrate that these types of attacks are effective in achieving their task and managing to fool common federated defenses (stealth). We also propose a mechanism to mitigate these attacks based on clean label training on the server side. In more detail, we see that the model degradation attack causes an accuracy degradation of up to 25%, while common defenses can only alleviate this for a percentage of ∼5%. Similarly, the targeted label attack results in a misclassification of 56% compared to 2.5% when no attack takes place. Moreover, our proposed defense mechanism is able to mitigate these attacks. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence)
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Review

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27 pages, 1135 KiB  
Review
A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective
by Amin Avan, Akramul Azim and Qusay H. Mahmoud
Electronics 2023, 12(12), 2599; https://doi.org/10.3390/electronics12122599 - 8 Jun 2023
Cited by 4 | Viewed by 3368
Abstract
The edge computing paradigm enables mobile devices with limited memory and processing power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications on the network by bringing the computational power and storage capacity closer to end users. Edge computing comprises heterogeneous computing platforms with resource [...] Read more.
The edge computing paradigm enables mobile devices with limited memory and processing power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications on the network by bringing the computational power and storage capacity closer to end users. Edge computing comprises heterogeneous computing platforms with resource constraints that are geographically distributed all over the network. As users are mobile and applications change over time, identifying an optimal task scheduling method is a complex multi-objective optimization problem that is NP-hard, meaning the exhaustive search with a time complexity that grows exponentially can solve the problem. Therefore, various approaches are utilized to discover a good solution for scheduling the tasks within a reasonable time complexity, while achieving the most optimal solution takes exponential time. This study reviews task scheduling algorithms based on centralized and distributed methods in a three-layer computing architecture to identify their strengths and limitations in scheduling tasks to edge service nodes. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence)
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