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Electronics
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New Challenges in Internet of Things and Cloud-Fog-Edge Computing
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New Challenges in Internet of Things and Cloud-Fog-Edge Computing

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

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 2430

Special Issue Editors

Dr. Livio Pompianu

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: information security; blockchain analytics; smart contracts; cryptocurrency scams
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Sebastian Podda

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: artificial intelligence; deep learning; information security; financial forecasting; blockchain; smart contracts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing adoption of interconnected smart devices has led to the development of the Internet of Things (IoT). The peculiarities of smart devices from computers (particularly the need to limit energy and bandwidth consumption) have necessitated the development of substantial innovations in various areas of ICT related to the use of smart devices, such as architectures and computational models.

However, many limitations exist in various application environments. Therefore, this Special Issue seeks high-quality submissions highlighting emerging solutions in the Internet of Things, Cloud Computing, Fog Computing, and Edge Computing. In particular, this Special Issue covers, but is not limited to, the following topics:

  • Design, models and specifications of IoT and cloud-fog-edge computing systems;
  • Challenges and opportunities of IoT and cloud-fog-edge computing;
  • Innovative protocols and systems for device-to-cloud communication;
  • Facing high latency, network congestion, and loss of reliability;
  • Improving SLA, QoS, and QoE;
  • Efficient energy management;
  • Disruptive applications;
  • IoT devices and edge nodes security (e.g., authentication, access control, cyber-physical security);
  • Privacy issues and solutions;
  • Challenges in implementation, integration, and deployment.

Dr. Livio Pompianu
Dr. Alessandro Sebastian Podda
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

  • internet of things
  • cloud-fog-edge computing
  • challenges
  • smart systems

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Published Papers (1 paper)

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Research

16 pages, 8420 KiB  
Article
One-Stage Small Object Detection Using Super-Resolved Feature Map for Edge Devices
by Xuan Nghia Huynh, Gu Beom Jung and Jae Kyu Suhr
Electronics 2024, 13(2), 409; https://doi.org/10.3390/electronics13020409 - 18 Jan 2024
Cited by 1 | Viewed by 1810
Abstract
Despite the achievements of deep neural-network-based object detection, detecting small objects in low-resolution images remains a challenging task due to limited information. A possible solution to alleviate the issue involves integrating super-resolution (SR) techniques into object detectors, particularly enhancing feature maps for small-sized [...] Read more.
Despite the achievements of deep neural-network-based object detection, detecting small objects in low-resolution images remains a challenging task due to limited information. A possible solution to alleviate the issue involves integrating super-resolution (SR) techniques into object detectors, particularly enhancing feature maps for small-sized objects. This paper explores the impact of high-resolution super-resolved feature maps generated by SR techniques, especially for a one-stage detector that demonstrates a good compromise between detection accuracy and computational efficiency. Firstly, this paper suggests the integration of an SR module named feature texture transfer (FTT) into the one-stage detector, YOLOv4. Feature maps from the backbone and the neck of vanilla YOLOv4 are combined to build a super-resolved feature map for small-sized object detection. Secondly, it proposes a novel SR module with more impressive performance and slightly lower computation demand than the FTT. The proposed SR module utilizes three input feature maps with different resolutions to generate a super-resolved feature map for small-sized object detection. Lastly, it introduces a simplified version of an SR module that maintains similar performance while using only half the computation of the FTT. This attentively simplified module can be effectively used for real-time embedded systems. Experimental results demonstrate that the proposed approach substantially enhances the detection performance of small-sized objects on two benchmark datasets, including a self-built surveillance dataset and the VisDrone2019 dataset. In addition, this paper employs the proposed approach on an embedded system with a Qualcomm QCS610 and demonstrates its feasibility for real-time operation on edge devices. Full article
(This article belongs to the Special Issue New Challenges in Internet of Things and Cloud-Fog-Edge Computing)
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