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Entropy
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Task-Oriented Communications in Industrial IoT: Age of Information and Beyond
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Task-Oriented Communications in Industrial IoT: Age of Information and Beyond

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 2428

Special Issue Editors

Dr. Jie Cao

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Interests: semantic communication; age of information; sensing-communication-control co-design; industrial metaverse
Dr. Xin Xie

E-Mail Website
Guest Editor
Key Laboratory of Industrial Internet of Things and Networked Control, Chongqing University of Posts and Telecommunications, Chongqing, China
Interests: age of information; industrial Internet of Things; real-time scheduling; Internet of Vehicles; edge computing; millimeter wave communications
Dr. Zhongxiang Wei

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Tongji University, Shanghai, China
Interests: energy-efficient communication; anonymous communication; IoT; algorithm design and optimization; massive MIMO

Special Issue Information

Dear Colleagues,

The Age of Information (AoI) has become an important metric for evaluating the real-time performance of systems where the timely delivery of information is critical, such as the Industrial Internet of Things (IoT), intelligent manufacturing and intelligent control systems. As technology advances, maintaining a low AoI becomes increasingly challenging due to the complexity and dynamics of these environments. This Special Issue aims to present cutting-edge research that addresses these challenges by optimizing AoI, improving data freshness and enhancing decision making through smart and efficient communication strategies. In particular, we welcome contributions that investigate innovative approaches to AoI minimization, task-driven communication frameworks and advanced latency reduction techniques. In addition, we encourage case studies on the application of these technologies in industrial contexts. By bringing together pioneering research in this area, we aim to advance the understanding and implementation of instant messaging strategies in dynamic, high-demand industrial environments. Please submit your work to contribute to this rapidly evolving field and help shape the future of industrial communication systems.

Dr. Jie Cao
Dr. Xin Xie
Dr. Zhongxiang Wei
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. Entropy is an international peer-reviewed open access monthly 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

  • industrial IoT
  • Age of Information
  • task-oriented communications
  • real-time communication

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Published Papers (4 papers)

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Research

20 pages, 1449 KiB  
Article
Deep Reinforcement Learning-Based Resource Allocation for UAV-GAP Downlink Cooperative NOMA in IIoT Systems
by Yuanyan Huang, Jingjing Su, Xuan Lu, Shoulin Huang, Hongyan Zhu and Haiyong Zeng
Entropy 2025, 27(8), 811; https://doi.org/10.3390/e27080811 - 29 Jul 2025
Viewed by 372
Abstract
This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)–ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal [...] Read more.
This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)–ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor–Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces. Closed-form power allocation and maximum weight bipartite matching are integrated to enable efficient user pairing and resource management. Simulation results show that the proposed scheme significantly enhances system performance in terms of throughput, spectral efficiency, and interference management, while enabling robustness against channel uncertainties in dynamic IIoT environments. The findings indicate that combining model-free reinforcement learning with conventional optimization provides a viable solution for adaptive resource management in dynamic UAV-GAP cooperative communication scenarios. Full article
(This article belongs to the Special Issue Task-Oriented Communications in Industrial IoT: Age of Information and Beyond)
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19 pages, 788 KiB  
Article
Age of Information Minimization in Multicarrier-Based Wireless Powered Sensor Networks
by Juan Sun, Jingjie Xia, Shubin Zhang and Xinjie Yu
Entropy 2025, 27(6), 603; https://doi.org/10.3390/e27060603 - 5 Jun 2025
Viewed by 482
Abstract
This study investigates the challenge of ensuring timely information delivery in wireless powered sensor networks (WPSNs), where multiple sensors forward status-update packets to a base station (BS). Time is partitioned to multiple time blocks, with each time block dedicated to either data packet [...] Read more.
This study investigates the challenge of ensuring timely information delivery in wireless powered sensor networks (WPSNs), where multiple sensors forward status-update packets to a base station (BS). Time is partitioned to multiple time blocks, with each time block dedicated to either data packet transmission or energy transfer. Our objective is to minimize the long-term average weighted sum of the Age of Information (WAoI) for physical processes monitored by sensors. We formulate this optimization problem as a multi-stage stochastic optimization program. To tackle this intricate problem, we propose a novel approach that leverages Lyapunov optimization to transform the complex original problem into a sequence of per-time-bock deterministic problems. These deterministic problems are then solved using model-free deep reinforcement learning (DRL). Simulation results demonstrate that our proposed algorithm achieves significantly lower WAoI compared to the DQN, AoI-based greedy, and energy-based greedy algorithms. Furthermore, our method effectively mitigates the issue of excessive instantaneous AoI experienced by individual sensors compared to the DQN. Full article
(This article belongs to the Special Issue Task-Oriented Communications in Industrial IoT: Age of Information and Beyond)
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12 pages, 2019 KiB  
Article
A Zero-Touch Dynamic Configuration Management Framework for Time-Sensitive Networking (TSN)
by Junhui Jiang, Shanyu Jin, Xinghan Li, Kaisong Zhang and Baodan Sun
Entropy 2025, 27(6), 584; https://doi.org/10.3390/e27060584 - 30 May 2025
Viewed by 527
Abstract
As Industry 5.0 progresses, the demand for zero-touch configuration in industrial automation and smart manufacturing is increasing. This paper proposes a dynamic configuration management framework for Time-Sensitive Networking (TSN), aiming to address the challenges of flexibility and adaptability in dynamic network environments. A [...] Read more.
As Industry 5.0 progresses, the demand for zero-touch configuration in industrial automation and smart manufacturing is increasing. This paper proposes a dynamic configuration management framework for Time-Sensitive Networking (TSN), aiming to address the challenges of flexibility and adaptability in dynamic network environments. A zero-touch configuration model is presented for TSN by incorporating a Delay-Aware Shortest Path Search (DASPS) algorithm to improve scheduling success rates. Simulation results demonstrate the ability of the framework to reconfigure networks within 2.67 milliseconds. The DASPS algorithm achieves a scheduling success rate of 70.22% for 1000 TSN flows, in contrast to only 22.23% achieved by the Shortest Path Search (SPS) algorithm. The proposed model effectively adapts to dynamic network changes, guaranteeing real-time data transmission. To further evaluate system adaptability, path entropy is introduced as a metric to quantitatively assess the balance of scheduling outcomes under topological changes. In the event of link failures, path entropy experiences a sharp decline but rapidly recovers after reconfiguration, demonstrating the system’s strong self-healing capability. Full article
(This article belongs to the Special Issue Task-Oriented Communications in Industrial IoT: Age of Information and Beyond)
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17 pages, 1284 KiB  
Article
Entropy-Based Age-Aware Scheduling Strategy for UAV-Assisted IoT Data Transmission
by Lulu Jing, Hai Wang, Zhen Qin and Peng Zhu
Entropy 2025, 27(6), 578; https://doi.org/10.3390/e27060578 - 29 May 2025
Viewed by 509
Abstract
This paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed [...] Read more.
This paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed IoT devices (e.g., temperature readings in a real-time forest fire monitoring system) and forwards it to the base station. To capture the impact of data staleness, a novel Age of Information (AoI) and entropy-aware system loss is defined in terms of L-conditional cross-entropy, which quantifies the expected penalty caused by state misestimation. The scheduling problem, which aims to minimize the system loss defined by L-conditional cross-entropy, is formulated as a Restless Multi-Armed Bandit (RMAB) problem. By applying Lagrange relaxation, the objective function is decomposed into tractable sub-problems, enabling a low-complexity, gain-index-based scheduling strategy. Numerical simulations validate the effectiveness of the proposed algorithm in reducing the long-term average system loss. In particular, the gain-index-based policy achieves a significant reduction in average penalty compared to random, round-robin, periodic update, and MAX-AoI scheduling strategies, demonstrating its superior performance over these baselines. Full article
(This article belongs to the Special Issue Task-Oriented Communications in Industrial IoT: Age of Information and Beyond)
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