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Applied Sciences
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Secure Provisioning Services in Cloud-Edge Systems
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Secure Provisioning Services in Cloud-Edge Systems

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 (28 July 2022) | Viewed by 5101

Special Issue Editor

Prof. Dr. Valentina Casola

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology of the University of Naples Federico II, Napoli, Italy
Interests: design and evaluate distributed systems; including cyber-physical infrastructures; cloud systems; IoT; web services

Special Issue Information

Dear Colleagues,

Cloud computing, edge computing, and IoT have significantly changed from the original architectural provisional models with a pure adoption of virtual resources (and services) towards a transparent – and adaptive – hosting environment where cloud providers, as well as “on-premise” resources and end-nodes, fully realize the “everything-as-a-service” provisioning concept.

With edge computing, data storage and computation power may be optimally distributed closer to the data source or in the cloud, to eliminate lag-times or save bandwidth, but new security challenges arise. The optimal secure design of these architectures, including the selection of optimal services to acquire, is not-trivial in the cloud–edge context, due to the involvement of several constraints and types of cloud resource offerings, and the impact on cost, performance, and safety.

This Special Issue is focused on foundations, methodologies, and mechanisms that support the provision of secure services in the new cloud–edge architectural models through design, modeling, and evaluation of systems.

Prof. Dr. Valentina Casola
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

  • methods and tools to provide safe and secure critical software systems in edge computing
  • quality metrics for safety and security in edge computing
  • cloud, IoT, and edge security
  • SLAs for safety, security, and privacy in edge computing

Published Papers (2 papers)

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Research

18 pages, 2095 KiB  
Article
A Fuzzy-Based Method for Objects Selection in Blockchain-Enabled Edge-IoT Platforms Using a Hybrid Multi-Criteria Decision-Making Model
by Bhaskar B. Gardas, Arash Heidari, Nima Jafari Navimipour and Mehmet Unal
Appl. Sci. 2022, 12(17), 8906; https://doi.org/10.3390/app12178906 - 05 Sep 2022
Cited by 17 | Viewed by 2034
Abstract
The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud [...] Read more.
The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud server load, slow response times, and a lack of global mobility. Some of these flaws can be addressed with edge computing. In addition, node selection helps avoid common difficulties related to IoT, including network lifespan, allocation of resources, and trust in the acquired data by selecting the correct nodes at a suitable period. On the other hand, the IoT’s interconnection of edge and blockchain technologies gives a fresh perspective on access control framework design. This article provides a novel node selection approach for blockchain-enabled edge IoT that provides a quick and dependable node selection. Moreover, fuzzy logic to approximation logic was used to manage numerical and linguistic data simultaneously. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. The suggested fuzzy-based technique employs three input criteria to select the correct IoT node for a given mission in IoT-edge situations. The outcomes of the experiments indicate that the proposed framework enhances the parameters under consideration. Full article
(This article belongs to the Special Issue Secure Provisioning Services in Cloud-Edge Systems)
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26 pages, 6289 KiB  
Article
Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenarios
by Arash Heidari, Mohammad Ali Jabraeil Jamali, Nima Jafari Navimipour and Shahin Akbarpour
Appl. Sci. 2022, 12(16), 8232; https://doi.org/10.3390/app12168232 - 17 Aug 2022
Cited by 29 | Viewed by 2213
Abstract
The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive [...] Read more.
The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja’s simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average. Full article
(This article belongs to the Special Issue Secure Provisioning Services in Cloud-Edge Systems)
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