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Electronics
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Intelligent IoT Systems with Mobile/Multi-Access Edge Computing (MEC)
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Intelligent IoT Systems with Mobile/Multi-Access Edge Computing (MEC)

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

Deadline for manuscript submissions: closed (15 May 2025) | Viewed by 5118

Special Issue Editors

Dr. Evangelos Maltezos

E-Mail Website
Guest Editor
I-SENSE Group of the Institute of Communication and Computer Systems (ICCS), 15773 Zografou, Greece
Interests: smart sensor-based situational awareness and integration; edge computing; crisis management; computer vision; image and point cloud processing; machine learning; earth observation; terrestrial and UAV embedded systems; IoT systems
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Katsaros

E-Mail Website
Guest Editor
I-SENSE Group of the Institute of Communication and Computer Systems (ICCS), 15773 Zografou, Greece
Interests: (beyond) 5G technologies; management and orchestration in the compute continuum; (extreme/far/near) edge computing; federated learning; predictive QoS
Dr. Eleftherios Ouzounoglou

E-Mail Website
Guest Editor
I-SENSE Group of the Institute of Communication and Computer Systems (ICCS), 15773 Zografou, Greece
Interests: crisis management; security; data handling; sensor integration and communications security issues; systems biology; bioinformatics, machine learning; multiscale cancer modelling and simulation; situational awareness; data and communications security and management
Special Issues, Collections and Topics in MDPI journals
Dr. Angelos Amditis

E-Mail Website
Guest Editor
I-SENSE Group of the Institute of Communication and Computer Systems (ICCS), 15773 Zografou, Greece
Interests: automated transport systems; cooperative systems; smart mobility; sensor and information fusion; communication technologies; human–machine interaction; IoT systems and platforms; cloud computing and services; environmental and security applications; smart embedded and cyber physical systems; crisis management emergency communications; radar systems; computer vision and detection; sensor-based situational awareness; 5G networks; machine learning; security; VR/AR; artificial intelligence; circular economy and block chain
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New technologies such as mobile and multi-access edge computing (MEC) have gained significant attention in recent years, offering flexible and viable solutions that overcome the limitations of centralized cloud computing. Indeed, by enabling computation at the network edge, MEC realizes a series of advantages for applications, including  (i) context awareness; (ii) geo-distributed capabilities; and (iii) low latency.

Within this broad context, edge computing can take different forms subject to the exact definition of the edge, i.e., computation can be performed at a regional level, at (5G) mobile network base stations or even directly on an end device. This is possible due to advances in several domains including microelectronics, as well as artificial intelligence (AI) and machine learning (ML), together facilitating the emergence of a multitude of applications that increasingly bridge the gap between the cyber and the physical worlds. Examples include, but are not limited to, augmented reality (AR), virtual reality (VR), digital twins (DT), virtual simulations, manufacturing/Industry 4.0, connected automated mobility (CAM)/ automotive, etc.

In this Special Issue, original research articles and reviews focusing on the support of intelligent IoT solutions and edge computing are welcome. Research areas may include (but are not limited to) the following:

  • Novel system architectures for the support of intelligent IoT applications at the edge;
  • New programming models for intelligent IoT applications in the compute continuum;
  • Novel intelligent IoT applications/services on top of MEC platforms;
  • Experimental performance evaluation/trials of MEC-enabled intelligent IoT applications;
  • Service/resource management and orchestration solutions for the support of intelligent IoT services/applications at the edge;
  • Applications of (distributed) machine learning (ML) techniques for the edge, e.g., federated learning, split learning, etc.;
  • Software optimizations for the support of AI/ML workloads at the edge, e.g., Tiny ML applications.

We look forward to receiving your contributions. 

Dr. Evangelos Maltezos
Dr. Konstantinos Katsaros
Dr. Eleftherios Ouzounoglou
Dr. Angelos Amditis
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

  • autonomous vehicles
  • mixed reality (MR)/ augmented reality (AR), virtual reality (VR)
  • computer vision and video analytics
  • AI/machine learning
  • systems integration and mobile or multi-access edge computing (MEC)
  • 5G/(Beyond) 5G
  • IoT/ AIoT
  • smart manufacturing
  • triage/wearables
  • situational awareness
  • UxVs

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Published Papers (3 papers)

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Research

33 pages, 6915 KiB  
Article
AI-Driven Resource Allocation and Auto-Scaling of VNFs in Edge-5G-IoT Ecosystems
by Rafael Moreno-Vozmediano, Eduardo Huedo, Rubén S. Montero and Ignacio M. Llorente
Electronics 2025, 14(9), 1808; https://doi.org/10.3390/electronics14091808 - 28 Apr 2025
Viewed by 218
Abstract
With the rapid expansion of edge-5G-IoT ecosystems, the need for intelligent and adaptive resource management strategies has become a critical challenge. In these environments, Virtualized Network Functions (VNFs) deployed at the network edge must handle highly dynamic workloads, making fixed resource allocation inefficient. [...] Read more.
With the rapid expansion of edge-5G-IoT ecosystems, the need for intelligent and adaptive resource management strategies has become a critical challenge. In these environments, Virtualized Network Functions (VNFs) deployed at the network edge must handle highly dynamic workloads, making fixed resource allocation inefficient. While over-provisioning can lead to unnecessary resource waste, an especially critical issue in edge environments with limited resources, under-provisioning can degrade performance and service quality. This paper presents an AI-based predictive auto-scaling framework designed to optimize resource allocation for VNFs in edge/5G-enabled IoT environments. The proposed approach evaluates and integrates different ML-based regression models to characterize VNF resource consumption, along with various forecasting methods to anticipate future workload fluctuations, enabling both vertical and horizontal auto-scaling. Extensive experiments with real-world traffic data demonstrate the effectiveness of our approach, showing significant improvements in resource efficiency compared to fixed allocation strategies. Full article
(This article belongs to the Special Issue Intelligent IoT Systems with Mobile/Multi-Access Edge Computing (MEC))
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19 pages, 3729 KiB  
Article
GraphSensor: A Graph Attention Network for Time-Series Sensor
by Jiaqi Ge, Gaochao Xu, Jianchao Lu, Xu Xu and Xiangyu Meng
Electronics 2024, 13(12), 2290; https://doi.org/10.3390/electronics13122290 - 11 Jun 2024
Cited by 2 | Viewed by 1501
Abstract
Our work focuses on the exploration of the internal relationships of signals in an individual sensor. In particular, we address the problem of not being able to evaluate such intrasensor relationships due to missing rich and explicit feature representation. To solve this problem, [...] Read more.
Our work focuses on the exploration of the internal relationships of signals in an individual sensor. In particular, we address the problem of not being able to evaluate such intrasensor relationships due to missing rich and explicit feature representation. To solve this problem, we propose GraphSensor, a graph attention network, with a shared-weight convolution feature encoder to generate the signal segments and learn the internal relationships between them. Furthermore, we enrich the representation of the features by utilizing a multi-head approach when creating the internal relationship graph. Compared with traditional multi-head approaches, we propose a more efficient convolution-based multi-head mechanism, which only requires 56% of model parameters compared with the best multi-head baseline as demonstrated in the experiments. Moreover, GraphSensor is capable of achieving state-of-the-art performance in the electroencephalography dataset and improving the accuracy by 13.8% compared to the best baseline in an inertial measurement unit (IMU) dataset. Full article
(This article belongs to the Special Issue Intelligent IoT Systems with Mobile/Multi-Access Edge Computing (MEC))
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22 pages, 2929 KiB  
Article
A DRL-Based Task Offloading Scheme for Server Decision-Making in Multi-Access Edge Computing
by Ducsun Lim and Inwhee Joe
Electronics 2023, 12(18), 3882; https://doi.org/10.3390/electronics12183882 - 14 Sep 2023
Cited by 14 | Viewed by 2078
Abstract
Multi-access edge computing (MEC), based on hierarchical cloud computing, offers abundant resources to support the next-generation Internet of Things network. However, several critical challenges, including offloading methods, network dynamics, resource diversity, and server decision-making, remain open. Regarding offloading, most conventional approaches have neglected [...] Read more.
Multi-access edge computing (MEC), based on hierarchical cloud computing, offers abundant resources to support the next-generation Internet of Things network. However, several critical challenges, including offloading methods, network dynamics, resource diversity, and server decision-making, remain open. Regarding offloading, most conventional approaches have neglected or oversimplified multi-MEC server scenarios, fixating on single-MEC instances. This myopic focus fails to adapt to computational offloading during MEC server overload, rendering such methods sub-optimal for real-world MEC deployments. To address this deficiency, we propose a solution that employs a deep reinforcement learning-based soft actor-critic (SAC) approach to compute offloading and facilitate MEC server decision-making in multi-user, multi-MEC server environments. Numerical experiments were conducted to evaluate the performance of our proposed solution. The results demonstrate that our approach significantly reduces latency, enhances energy efficiency, and achieves rapid and stable convergence, thereby highlighting the algorithm’s superior performance over existing methods. Full article
(This article belongs to the Special Issue Intelligent IoT Systems with Mobile/Multi-Access Edge Computing (MEC))
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