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Integrated Sensing, Communication, and Computing Networks for IoT Applications
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Integrated Sensing, Communication, and Computing Networks for IoT Applications

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

Deadline for manuscript submissions: 25 June 2025 | Viewed by 6127

Special Issue Editors

Dr. Xiaojie Wang

E-Mail Website
Guest Editor
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: Internet of Things; edge learning; UAV communications
Dr. Lu Sun

E-Mail Website
Guest Editor
Department of Communication Engineering, Institute of Information Science Technology, Dalian Maritime University, Dalian 116026, China
Interests: wireless communication; edge computing; wireless energy transmission; resource allocation; computational intelligence algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the number of new IoT applications has been multiplying unprecedentedly, such as digital twins, extended reality, and autonomous systems. This prompts a novel design paradigm of integrated sensing, communication, and computing networks which can provide data generation, exchanging and processing data in a comprehensive manner by jointly managing sensing, communication, and computing resources. However, there are still many challenges with such an integrated network, including a proper integrated sensing, communication, and computing protocol, the trade-off among different dimensional metrics, and the deployment of servers, sensors, and IoT devices. Therefore, this Special Issue aims to seek high-quality papers from academics and industry-related researchers of ubiquitous IoT, integrated sensing, communication, and computing resource management, who conduct research on the development of resource management and networking technologies for IoT systems and applications and present the most recently advanced methods and applications. Potential topics include, but are not limited to, the following:

  • Integrated sensing and communication technologies for IoT systems;
  • Integrated communication and computing technologies for IoT systems;
  • Integrated sensing, communication, and computing technologies for IoT systems;
  • The platform and testbed for integrated sensing, communication, and computing networks;
  • Resource management for IoT systems;
  • The development of novel IoT applications;
  • Security and privacy for IoT systems;
  • Novel technologies for IoT systems

Dr. Xiaojie Wang
Dr. Lu Sun
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. 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.

Keywords

  • sensors
  • communication
  • computing
  • IoT applications

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Published Papers (3 papers)

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19 pages, 555 KiB  
Article
Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks
by Khalid Ibrahim Qureshi, Bingxian Lu, Cheng Lu, Muhammad Ali Lodhi and Lei Wang
Sensors 2024, 24(20), 6535; https://doi.org/10.3390/s24206535 - 10 Oct 2024
Cited by 1 | Viewed by 1560
Abstract
In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions [...] Read more.
In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem’s non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios. Full article
(This article belongs to the Special Issue Integrated Sensing, Communication, and Computing Networks for IoT Applications)
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19 pages, 575 KiB  
Article
Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System
by Danxia Qiao, Lu Sun, Dianju Li, Huajie Xiong, Rina Liang, Zhenyuan Han and Liangtian Wan
Sensors 2024, 24(18), 6123; https://doi.org/10.3390/s24186123 - 22 Sep 2024
Cited by 2 | Viewed by 1720
Abstract
With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission [...] Read more.
With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to “double near and far effect” in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the “double near and far effect” and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG. Full article
(This article belongs to the Special Issue Integrated Sensing, Communication, and Computing Networks for IoT Applications)
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Other

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37 pages, 9046 KiB  
Systematic Review
Machine Learning-Based Resource Management in Fog Computing: A Systematic Literature Review
by Fahim Ullah Khan, Ibrar Ali Shah, Sadaqat Jan, Shabir Ahmad and Taegkeun Whangbo
Sensors 2025, 25(3), 687; https://doi.org/10.3390/s25030687 - 23 Jan 2025
Cited by 1 | Viewed by 2143
Abstract
This systematic literature review analyzes machine learning (ML)-based techniques for resource management in fog computing. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, this paper focuses on ML and deep learning (DL) solutions. Resource management in the fog computing [...] Read more.
This systematic literature review analyzes machine learning (ML)-based techniques for resource management in fog computing. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, this paper focuses on ML and deep learning (DL) solutions. Resource management in the fog computing domain was thoroughly analyzed by identifying the key factors and constraints. A total of 68 research papers of extended versions were finally selected and included in this study. The findings highlight a strong preference for DL in addressing resource management challenges within a fog computing paradigm, i.e., 66% of the reviewed articles leveraged DL techniques, while 34% utilized ML. Key factors such as latency, energy consumption, task scheduling, and QoS are interconnected and critical for resource management optimization. The analysis reveals that latency, energy consumption, and QoS are the prime factors addressed in the literature on ML-based fog computing resource management. Latency is the most frequently addressed parameter, investigated in 77% of the articles, followed by energy consumption and task scheduling at 44% and 33%, respectively. Furthermore, according to our evaluation, an extensive range of challenges, i.e., computational resource and latency, scalability and management, data availability and quality, and model complexity and interpretability, are addressed by employing 73, 53, 45, and 46 ML/DL techniques, respectively. Full article
(This article belongs to the Special Issue Integrated Sensing, Communication, and Computing Networks for IoT Applications)
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