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Advanced Management of Fog/Edge Networks and IoT Sensors Devices
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Advanced Management of Fog/Edge Networks and IoT Sensors Devices

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 10882

Special Issue Editor

Dr. Rocío Pérez de Prado

E-Mail Website
Guest Editor
Department of Telecommunication Engineering, University of Jaén, 23071 Jaén, Spain
Interests: consumption; data centers; scientific workflows; machine learning; soft computing; artificial intelligence; optical communications; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, an efficient interplay of the different computing and storage capabilities of Fog/Edge networks and IoT sensor devices is a fundamental challenge that needs to be overcome in order to give rise to the highly-demanded integrated services. In spite of the advances in the separate areas of Fog/Edge Networks (typically associated with Cloud infrastructures) and IoT sensor devices, research in the interplay between these areas is still in its initial stages, and we have a long way to go to achieve their global management and harnessing. Particularly, the incorporation and design of intelligent strategies in the management, analysis, and use of the interconnection and planning of networks by Soft-Computing, Big Data or Machine Learning must be regarded as especially important for a deep transformation of and advancement in current associated technologies. The objective of this Special Issue is to support the study, analysis, and implementation of diverse enabling advances in the field of Fog/Edge networks and IoT sensor devices and their interconnection, such as the improvement of virtualization of applications and microservices in IoT sensor devices and Fog/Edge systems, the compatible integration of containers with the main function and performance of containers in IoT sensor devices and Fog/Edge equipment, the security in Fog/Edge and IoT device transactions with Blockchain technology, the improvement of lightweight virtualization systems for deployment in low-performance nodes, such as sensor devices of Fog/Edge networks and IoT or end users, energy reduction in the different layers of a Fog/Edge and IoT network through knowledge-based strategies, accelerating workflow processing in Fog systems, distributed data storage and Big Data tools, intelligent scheduling for container allocation, etc.

Topics to be covered include but are not limited to the following:

  • IoT sensor devices
  • Fog Computing
  • Edge Computing
  • Cloud Computing
  • Fog/Edge and IoT Networks Interplay
  • Content delivery networks
  • Soft-computing
  • Big Data
  • Machine learning
  • Virtualization
  • Containers
  • Scheduling
  • Blockchain
  • Energy consumption in computing distributed networks
  • Latency-aware application in distributed networks

Dr. Rocío Pérez de Prado
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. Sensors 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 2600 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.

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Published Papers (3 papers)

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Research

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23 pages, 787 KiB  
Article
Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network
by Dedi Triyanto, I Wayan Mustika and Widyawan
Sensors 2025, 25(6), 1722; https://doi.org/10.3390/s25061722 - 10 Mar 2025
Viewed by 207
Abstract
Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration [...] Read more.
Mobile edge computing (MEC) is a modern technique that has led to substantial progress in wireless networks. To address the challenge of efficient task implementation in resource-limited environments, this work strengthens system performance through resource allocation based on fairness and energy efficiency. Integration of energy-harvesting (EH) technology with MEC improves sustainability by optimizing the power consumption of mobile devices, which is crucial to the efficiency of task execution. The combination of MEC and an ultra-dense network (UDN) is essential in fifth-generation networks to fulfill the computing requirements of ultra-low-latency applications. In this study, issues related to computation offloading and resource allocation are addressed using the Lyapunov mixed-integer linear programming (MILP)-based optimal cost (LYMOC) technique. The optimization problem is solved using the Lyapunov drift-plus-penalty method. Subsequently, the MILP approach is employed to select the optimal offloading option while ensuring fairness-oriented resource allocation among users to improve overall system performance and user satisfaction. Unlike conventional approaches, which often overlook fairness in dense networks, the proposed method prioritizes fairness-oriented resource allocation, preventing service degradation and enhancing network efficiency. Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov–MILP-based short-distance complete local execution algorithm and the full offloading-computation method. Full article
(This article belongs to the Special Issue Advanced Management of Fog/Edge Networks and IoT Sensors Devices)
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18 pages, 2498 KiB  
Article
A Hybrid Spider Monkey and Hierarchical Particle Swarm Optimization Approach for Intrusion Detection on Internet of Things
by Sandhya Ethala and Annapurani Kumarappan
Sensors 2022, 22(21), 8566; https://doi.org/10.3390/s22218566 - 7 Nov 2022
Cited by 20 | Viewed by 2573
Abstract
The Internet of Things (IoT) network integrates physical objects such as sensors, networks, and electronics with software to collect and exchange data. Physical objects with a unique IP address communicate with external entities over the internet to exchange data in the network. Due [...] Read more.
The Internet of Things (IoT) network integrates physical objects such as sensors, networks, and electronics with software to collect and exchange data. Physical objects with a unique IP address communicate with external entities over the internet to exchange data in the network. Due to a lack of security measures, these network entities are vulnerable to severe attacks. To address this, an efficient security mechanism for dealing with the threat and detecting attacks is necessary. The proposed hybrid optimization approach combines Spider Monkey Optimization (SMO) and Hierarchical Particle Swarm Optimization (HPSO) to handle the huge amount of intrusion data classification problems and improve detection accuracy by minimizing false alarm rates. After finding the best optimum values, the Random Forest Classifier (RFC) was used to classify attacks from the NSL-KDD and UNSW-NB 15 datasets. The SVM model obtained accuracy of 91.82%, DT of 98.99%, and RFC of 99.13%, and the proposed model obtained 99.175% for the NSL-KDD dataset. Similarly, SVM obtained accuracy of 85.88%, DT of 88.87%, RFC of 91.65%, and the proposed model obtained 99.18% for the UNSW NB-15 dataset. The proposed model achieved accuracy of 99.175% for the NSL-KDD dataset which is higher than the state-of-the-art techniques such as DNN of 97.72% and Ensemble Learning at 85.2%. Full article
(This article belongs to the Special Issue Advanced Management of Fog/Edge Networks and IoT Sensors Devices)
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Review

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21 pages, 3453 KiB  
Review
Smart Containers Schedulers for Microservices Provision in Cloud-Fog-IoT Networks. Challenges and Opportunities
by Rocío Pérez de Prado, Sebastián García-Galán, José Enrique Muñoz-Expósito, Adam Marchewka and Nicolás Ruiz-Reyes
Sensors 2020, 20(6), 1714; https://doi.org/10.3390/s20061714 - 19 Mar 2020
Cited by 19 | Viewed by 6313
Abstract
Docker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers’ scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intelligent containers’ schedulers based on soft-computing in [...] Read more.
Docker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers’ scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intelligent containers’ schedulers based on soft-computing in the dominant open-source containers’ management platforms: Docker Swarm, Google Kubernetes and Apache Mesos. Secondly, the need for specific intelligent containers’ schedulers for the different interfaces in cloud-fog-IoT networks: cloud-to-fog, fog-to-IoT and cloud-to-fog. The goal of this work is to support the optimal allocation of microservices provided by the main cloud service providers today and used by millions of users worldwide in applications such as smart health, content delivery networks, smart health, etc. Particularly, the improvement is studied in terms of quality of service (QoS) parameters such as latency, load balance, energy consumption and runtime, based on the analysis of previous works and implementations. Moreover, the scientific-technical impact of smart containers’ scheduling in the market is also discussed, showing the possible repercussion of the raised opportunities in the research line. Full article
(This article belongs to the Special Issue Advanced Management of Fog/Edge Networks and IoT Sensors Devices)
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