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Applied Sciences
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Edge Intelligence and Green Communication Networks for IoT
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Edge Intelligence and Green Communication Networks for IoT

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 912

Special Issue Editors

Dr. Raul Parada

E-Mail Website
Guest Editor
Centre Tecnológic de Telecomunicacions de Catalunya, 08860 Barcelona, Spain
Interests: telecommunications; RFID; IoT; WSN; mmWave; 5G
Dr. Jože Guna

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: XR; user experience; interactions; multimedia; ICT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Edge intelligence and green communication networks are emerging as pivotal technologies in the development of the Internet of Things (IoT). As IoT devices proliferate, the need for efficient, low-latency, and sustainable network solutions becomes increasingly critical. Edge intelligence brings computation and data processing closer to the source of data generation, reducing latency, enhancing privacy, and improving the overall efficiency of IoT systems. Conversely, green communication networks emphasize energy efficiency and a reduction in carbon footprints, ensuring that the expansion of the IoT does not come at the expense of environmental sustainability.

The integration of edge intelligence with green communication networks holds the potential to revolutionize the IoT by enabling smart, adaptive, and environmentally-friendly systems. This convergence will not only support the massive scale of IoT devices but also ensure that these systems are resilient, secure, and sustainable. It paves the way for innovative applications across various domains such as smart cities, industrial automation, healthcare, and more.

This Special Issue aims to present the latest research and advancements in the fields of edge intelligence and green communication networks within the context of the IoT. Potential topics of interest include, but are not limited to, the following:

  • Edge computing architectures for the IoT;
  • Energy-efficient IoT communication protocols;
  • Green network design and optimization;
  • AI-driven energy management in IoT networks;
  • Low-latency communication strategies for edge IoT;
  • The integration of renewable energy sources in IoT networks;
  • Sustainable and scalable edge intelligence frameworks;
  • Security and privacy challenges in edge-enabled IoT;
  • Green 5G and beyond networks for IoT applications;
  • Edge intelligence in smart grids and smart cities;
  • Resource allocation and management in green IoT networks;
  • Machine learning for energy efficiency in IoT systems;
  • Future trends in green communication networks and edge computing.

We invite you to contribute to this Special Issue by submitting your research on the forefront of edge intelligence and green communication networks for the IoT, showcasing innovations that drive the future of sustainable and efficient IoT ecosystems.

Dr. Raul Parada
Dr. Jože Guna
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. 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

  • sustainable IoT architectures
  • energy harvesting in IoT
  • intelligent resource management
  • eco-friendly network infrastructures
  • decentralized edge computing
  • AI-driven network optimization
  • low-power IoT devices

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Published Papers (1 paper)

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Research

23 pages, 750 KiB  
Article
Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
by Qingqing Yuan and Zhihua Li
Appl. Sci. 2025, 15(3), 1097; https://doi.org/10.3390/app15031097 - 22 Jan 2025
Viewed by 727
Abstract
Computations performed by using convolutional layers in deep learning require significant resources; thus, their scope of applicability is limited. When deep neural network models are employed in an edge-computing system, the limited computational power and storage resources of edge devices can degrade inference [...] Read more.
Computations performed by using convolutional layers in deep learning require significant resources; thus, their scope of applicability is limited. When deep neural network models are employed in an edge-computing system, the limited computational power and storage resources of edge devices can degrade inference performance, require a considerable amount of computation time, and result in increased energy consumption. To address these issues, this study presents a convolutional-layer partitioning model, based on the fused tile partitioning (FTP) algorithm, for enhancing the distributed inference capabilities of edge devices. First, a resource-adaptive workload-partitioning optimization model is designed to promote load balancing across heterogeneous edge systems. Next, the FTP algorithm is improved, leading to a new layer-fused partitioning method that is used to solve the optimization model. The results of simulation experiments show that the proposed convolutional-layer partitioning method effectively improves the inference performance of edge devices. When five edge devices are used, the speed of the proposed method becomes 1.65–3.48 times those of existing algorithms. Full article
(This article belongs to the Special Issue Edge Intelligence and Green Communication Networks for IoT)
►▼ Show Figures

Figure 1

Figure 1
<p>System overview.</p> Full article ">Figure 2
<p>Different convolutional-layer partitioning strategies.</p> Full article ">Figure 3
<p>BasicBlock structure transformed into sub-fused blocks.</p> Full article ">Figure 4
<p>MAC metric.</p> Full article ">Figure 5
<p>Latency metric.</p> Full article ">Figure 6
<p>Energy metric.</p> Full article ">Figure 7
<p>Comparison between the latencies of different methods across four DNN models.</p> Full article ">Figure 8
<p>Comparison between the energy consumptions of different methods across four DNN models.</p> Full article ">Figure 9
<p>Comparison between the communication overheads of different algorithms on four edge devices.</p> Full article ">Figure 10
<p>The latency and energy consumption of the LFP algorithm vary with the number of devices. The top text indicates which type of device are newly added to the cluster.</p> Full article ">Figure 11
<p>The latency and energy consumption of the LFP algorithm over time.</p> Full article ">
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