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Artificial Intelligence of Things for Future Networks and Service Management
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Artificial Intelligence of Things for Future Networks and Service Management

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 4461

Special Issue Editor

Prof. Dr. Li Zhu

E-Mail Website
Guest Editor
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100082, China
Interests: artificial intelligence; intelligent traffic control and optimization; traffic system perception and big data; control engineering; traffic system simulation and testing; electronic information technology; network and information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Future networks like 5G networks, space–air–ground integrated networks, and large-scale distributed networks, will face numerous innovations and improvements. With the large-scale deployment of Internet of Things (IoT) applications, millions of edge devices are connected to the Internet to provide high-quality services for people’s digital lives. The IoT, nevertheless, poses difficulties in existing networks to meet the quality of experience of subscribers, and traditional communication and computing approaches might fail to meet future network requirements.

Recently, as a collaborative application of artificial intelligence (AI) and IoT techniques, the Artificial Intelligence of Things (AIoT), has been proposed to improve the management and optimization of future networks. The introduction of AIoT can boost the network to monitor the traffic in real time, quickly identify abnormalities, and dynamically adjust the configuration, thereby ensuring high-speed and reliable transmission. For example, for complex networks with high loads and constrained resources, the AI algorithm, e.g., reinforcement learning, can optimize resource allocation and scheduling to meet the future network’s requirements. In addition, deep learning methods can also detect and respond to potential dynamics in real time so that proactive measures can be taken to ensure the network performance. Furthermore, the AIoT can also facilitate the implementation of advanced authentication and access control mechanisms to ensure the integrity and privacy of data through IoT devices.

The AIoT provides new ideas for designing and optimizing future networks by integrating AI and the IoT, which would bring about the intelligent decision-making, efficient communication, and precise management of connected devices. This Special Issue will focus on topics including but not limited to the latest research advents in designing, evaluating, and optimizing future networks using the AIoT technique.

Prof. Dr. Li Zhu
Guest Editor

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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

  • Internet of Things (IoT)
  • Artificial Intelligence (AI)
  • future networks

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Published Papers (4 papers)

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Research

23 pages, 6069 KB  
Article
An Intelligent Obstacle Detection Method for Rail Transit Scenarios
by Zhao Sheng, Tianyang Liu, Wei Shangguan, Yijing Wang, Yige Wang and Zhiyu He
Sensors 2026, 26(5), 1673; https://doi.org/10.3390/s26051673 - 6 Mar 2026
Cited by 1 | Viewed by 625
Abstract
Traditional signal equipment is incapable of real-time monitoring of foreign objects intruding into track zones. To effectively ensure the operational safety of trains, this paper presents an intelligent obstacle detection approach of visual sensing for railway track regions based on YOLOv8, named ACX-YOLOv8. [...] Read more.
Traditional signal equipment is incapable of real-time monitoring of foreign objects intruding into track zones. To effectively ensure the operational safety of trains, this paper presents an intelligent obstacle detection approach of visual sensing for railway track regions based on YOLOv8, named ACX-YOLOv8. Built upon the baseline YOLOv8 framework, the proposed method first incorporates the spatial coordinate attention mechanism (SCAM) to enhance the model’s ability to capture long-range dependencies and local fine-grained details, thereby improving its perceptual capacity and feature representation performance. Subsequently, the cascaded dilated convolution (CDConv) module is integrated to effectively extract multi-scale image features, strengthening the model’s capability to identify foreign objects in complex railway environments. Finally, an X6 decoupled detection head is devised to further elevate the model’s detection accuracy and inference efficiency. Field experiments in real-world scenarios are conducted to validate the effectiveness of the improved algorithm. Experimental results demonstrate that the optimized ACX-YOLOv8 model has a total parameter count of 4.85 million and achieves a mean average precision at IoU = 0.5 (mAP50) of 87.1% on the test dataset, which is a 2.7% improvement over the original YOLOv8 baseline model. The lightweight property and detection precision of the model are both effectively guaranteed. Furthermore, to verify the generalization ability of the algorithm, tests are performed on the public PASCAL VOC dataset, where the mAP50 value is increased by 1.8%. These findings indicate that the ACX-YOLOv8 algorithm can detect various foreign objects invading railway track areas rapidly and accurately. It provides efficient and reliable technical support for real-time obstacle monitoring in complex and variable railway track environments and contributes to enhancing the safety and intelligentization level of railway operations. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things for Future Networks and Service Management)
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30 pages, 4494 KB  
Article
An Uncertainty-Aware Bayesian Deep Learning Method for Automatic Identification and Capacitance Estimation of Compensation Capacitors
by Tongdian Wang and Pan Wang
Sensors 2026, 26(1), 279; https://doi.org/10.3390/s26010279 - 2 Jan 2026
Viewed by 1049
Abstract
This paper addresses the challenges of misclassification and reliability assessment in compensation capacitor detection under strong noise in high-speed railway track circuits. A hierarchical Bayesian deep learning framework is proposed, integrating multi-domain signal enhancement in the time, frequency, and time–frequency (TF) domains with [...] Read more.
This paper addresses the challenges of misclassification and reliability assessment in compensation capacitor detection under strong noise in high-speed railway track circuits. A hierarchical Bayesian deep learning framework is proposed, integrating multi-domain signal enhancement in the time, frequency, and time–frequency (TF) domains with bidirectional long short-term memory (BiLSTM) sequence modeling for robust feature extraction. Bayesian classification and regression based on Monte Carlo (MC) Dropout and stochastic weight averaging Gaussian (SWAG) enable posterior inference, confidence interval estimation, and uncertainty-aware prediction, while a rejection mechanism filters low-confidence outputs. Experiments on 8782 real-world segments from five railway lines show that the proposed method achieves 97.8% state-recognition accuracy, a mean absolute error of 0.084 μF, and an R2 of 0.96. It further outperforms threshold-based, convolutional neural network (CNN), and standard BiLSTM models in negative log-likelihood (NLL), expected calibration error (ECE), and overall calibration quality, approaching the theoretical 95% interval coverage. The framework substantially improves robustness, accuracy, and reliability, providing a viable solution for intelligent monitoring and safety assurance of compensation capacitors in track circuits. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things for Future Networks and Service Management)
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17 pages, 3550 KB  
Article
Edge Intelligence-Based Rail Transit Equipment Inspection System
by Lijia Tian, Hongli Zhao, Li Zhu, Hailin Jiang and Xinjun Gao
Sensors 2026, 26(1), 236; https://doi.org/10.3390/s26010236 - 30 Dec 2025
Cited by 1 | Viewed by 1000
Abstract
The safe operation of rail transit systems relies heavily on the efficient and reliable maintenance of their equipment, as any malfunction or abnormal operation may pose serious risks to transportation safety. Traditional manual inspection methods are often characterized by high costs, low efficiency, [...] Read more.
The safe operation of rail transit systems relies heavily on the efficient and reliable maintenance of their equipment, as any malfunction or abnormal operation may pose serious risks to transportation safety. Traditional manual inspection methods are often characterized by high costs, low efficiency, and susceptibility to human error. To address these limitations, this paper presents a rail transit equipment inspection system based on Edge Intelligence (EI) and 5G technology. The proposed system adopts a cloud–edge–end collaborative architecture that integrates Computer Vision (CV) techniques to automate inspection tasks; specifically, a fine-tuned YOLOv8 model is employed for object detection of personnel and equipment, while a ResNet-18 network is utilized for equipment status classification. By implementing an ETSI MEC-compliant framework on edge servers (NVIDIA Jetson AGX Orin), the system enhances data processing efficiency and network performance, while further strengthening security through the use of a 5G private network that isolates critical infrastructure data from the public internet, and improving robustness via distributed edge nodes that eliminate single points of failure. The proposed solution has been deployed and evaluated in real-world scenarios on Beijing Metro Line 6. Experimental results demonstrate that the YOLOv8 model achieves a mean Average Precision (mAP@0.5) of 92.7% ± 0.4% for equipment detection, and the ResNet-18 classifier attains 95.8% ± 0.3% accuracy in distinguishing normal and abnormal statuses. Compared with a cloud-centric architecture, the EI-based system reduces the average end-to-end latency for anomaly detection tasks by 45% (28.5 ms vs. 52.1 ms) and significantly lowers daily bandwidth consumption by approximately 98.1% (from 40.0 GB to 0.76 GB) through an event-triggered evidence upload strategy involving images and short video clips, highlighting its superior real-time performance, security, robustness, and bandwidth efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things for Future Networks and Service Management)
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18 pages, 1643 KB  
Communication
A Localization Enhancement Method Based on Direct-Path Identification and Tracking for Future Networks
by Yuhong Huang and Youping Zhao
Sensors 2025, 25(15), 4538; https://doi.org/10.3390/s25154538 - 22 Jul 2025
Viewed by 980
Abstract
Localization is one of the essential problems in the Internet of Things (IoT). Dynamic changes in the radio environment may lead to poor localization accuracy or discontinuous localization in non-line-of-sight (NLOS) scenarios. To address this problem, this paper proposes a localization enhancement method [...] Read more.
Localization is one of the essential problems in the Internet of Things (IoT). Dynamic changes in the radio environment may lead to poor localization accuracy or discontinuous localization in non-line-of-sight (NLOS) scenarios. To address this problem, this paper proposes a localization enhancement method based on direct-path identification and tracking. More specifically, the proposed method significantly reduces the range error and localization error by quickly identifying the line-of-sight (LOS) to NLOS transition and effectively tracking the direct path. In a large testing hall, localization experiments based on the ultra-wideband (UWB) signal have been carried out. Experimental results show that the proposed method achieves a root mean square localization error of less than 0.3 m along the user equipment (UE) movement trajectory with serious NLOS propagation conditions. Compared with conventional methods, the proposed method significantly improves localization accuracy while ensuring continuous localization. Full article
(This article belongs to the Special Issue Artificial Intelligence of Things for Future Networks and Service Management)
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