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Special Issue "Applications of Fog Computing and Edge Computing in IoT Systems"

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

Deadline for manuscript submissions: 31 July 2023 | Viewed by 728

Special Issue Editors

Associate Professor, School of Computing, SASTRA Deemed University, Thanjavur 613401, India
Interests: recommender systems; cloud computing; Internet of Things; context-aware computing; big data analytics; social network analysis
Special Issues, Collections and Topics in MDPI journals
Centre for Advanced Data Science (CADS), Vellore Institute of Technology, Chennai 600127, India
Interests: intelligent systems; Internet of Things; big data analytics; cyber-physical systems
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A, 43124 Parma, Italy
Interests: Internet of Things; smart agriculture; smart cities; big stream; data
Special Issues, Collections and Topics in MDPI journals
Faculty of Science and Engineering, University of Melbourne, Melbourne 3010, Australia
Interests: fog/edge computing; secure Internet of Things; software-defined networking; mobile cloud computing

Special Issue Information

Dear Colleagues,

The nature of fog and edge computing devices is evolving as a result of developments in the Internet of Things (IoT). A perfect storm has formed within the IoT ecosystem due to the availability of novel sensor interfaces, effective low-power digital processors, and high-bandwidth low-power communication protocols. A growing breadth of capabilities needs to be supported by next-generation Internet of Things end-nodes, including multisensory data processing and analysis, complicated system control schemes, and ultimately artificial intelligence. The development of wearable and implantable biomedical devices as well as autonomous, insect-sized drones and nanoscale devices for environmental sensing and continuous monitoring of structures, machinery, and power grids will be made possible by these new capabilities. As a result, computationally demanding jobs are increasingly being performed on extremely energy-efficient small-form-factor devices.

Additionally, the AI revolution is posing new, intriguing challenges and necessitates the investigation of novel HW–SW codesign methodologies and advanced optimization techniques for AI frameworks on resource-constrained processors. This revolution gains traction from the widespread use of deep learning. The huge volume of MAC operations and high-bandwidth data transfers needed for deep learning inference make digital architectures—especially those employed in edge devices—critical. Because of this, tools for adjusting and analyzing the performance of neural networks as well as architectural optimizations are crucial for the development of next-generation edge devices.

Modern smart digital objects are networked and distributed systems that make up the Internet of Things systems, made possible by the development of 5G technology. A smart electronic device is an IoT node that is connected to the outside world via a communication infrastructure, or network. IoT applications place heavy demands on a network’s computing and communication resources, which are limited in terms of bandwidth for device-to-cloud connection and in-device processing power. The goal of edge computing is to use computing power located close to IoT nodes to deliver services quickly and with fewer accesses to the cloud, which may be slow or even intermittent. Edge computing allows low-latency service delivery for both safety and mission-critical applications like autonomous decision-making and non-critical applications like infotainment by bringing computer resources to the edge in closer proximity to devices. This Special Issue seeks to give a place for discussing various facets of edge computing and IoT systems. Subjects of interest include, but are not restricted to:

  • Framework, and models for edge-computing-enabled IoT systems;
  • Frameworks and models for fog-computing-enabled IoT systems;
  • Resource management and computational offloading for edge-computing-enabled IoT systems;
  • Machine learning, deep learning and federated learning for edge-computing-enabled IoT systems;
  • Security and privacy for edge-computing-enabled IoT systems;
  • Energy-efficient and green computing for edge-computing-enabled IoT systems;
  • Autonomous driving assisted by edge-computing-enabled IoT systems;
  • Traffic monitoring and video analytics with edge-computing-enabled IoT systems;
  • Application case studies for edge-computing-enabled IoT systems;
  • Application case studies for fog-computing-enabled IoT systems.

Dr. Subramaniyaswamy V.
Dr. Logesh Ravi 
Dr. Luca Davoli
Dr. Laura Belli
Dr. Redowan Mahmud
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 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.


  • Internet of Things
  • fog computing
  • edge computing
  • intelligent systems
  • machine learning
  • deep learning
  • federated learning
  • computational intelligence

Published Papers (1 paper)

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Transfer Learning for Image-Based Malware Detection for IoT
Sensors 2023, 23(6), 3253; - 20 Mar 2023
Viewed by 386
The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent [...] Read more.
The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. In this paper, a novel ensemble model, Stacked Ensemble—autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models—autoencoder, GRU, and MLP—that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them. Full article
(This article belongs to the Special Issue Applications of Fog Computing and Edge Computing in IoT Systems)
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