Table of Content

AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques

Submission Deadline: 28 February 2023 Submit to Special Issue

Guest Editors

Dr. Han Wang, City University of Macau, China
Prof. Lingwei Xu, Qingdao University of Science and Technology, China
Prof. T. Aaron Gulliver, University of Victoria, Canada


5G has been fully commercialized. With the continuous penetration of 5G in the vertical industry, people's idea of 6G is gradually put on the agenda. Facing 2030 +, 6G will fully support the digitization of the whole world on the basis of 5G, and combine with the development of artificial intelligence (AI) and other technologies to promote the society to move towards the "digital twin" world of virtual and reality, and realize the beautiful vision of "digital twin and ubiquitous wisdom". 6G integrates the digital world and the physical world. It is no longer a simple communication transmission channel, but also can sense everything, so as to realize the intelligence of everything. 6G will become the network of sensors and machine learning, the data center is the brain, and machine learning will spread all over the network. The key feature of 6G is native AI, which is distributed all over sensor network. It optimizes and manages the communication sensor network. The communication sensor network can be self-generated and self-evolving.

AI technology supporting 6G will allow the creation of the "smart application layer" for interconnecting devices, from self-driving cars to medical implants to geo sensors, all of which can communicate with each other in real time. This wide coverage network will be supported by an "intelligent sensor layer", which will quickly collect and analyze a large amount of relevant data from these interconnected devices.

However, the research of intelligent sensor is still in its infancy, and there are some technical difficulties to be solved. This special section focuses on the application of intelligent sensor in AI assisted sensor networks to timely publish the research results of intelligent sensor based on AI, and promote the development of intelligent sensor key technology.

Potential topics include but are not limited to the following:

1.      AI-based intelligent sensor network modeling.

2.      PHY-layer intelligent sensor network enablers: massive MIMO, mmWave, full-duplex, NOMA, etc.

3.      AI-powered intelligent Network-layer protocols, frameworks, infrastructures, IoT devices.

4.      AI-based energy-efficiency/harvesting optimization modeling for intelligent sensor network.

5.      AI-based network security and privacy modeling for intelligent sensor network.

6.      Advance intelligent big data analytics in intelligent sensor network model.

7.      Intelligent sensor applications: smart home, smart E-health, smart cities, intelligent manufacturing, etc.

Published Papers

  • Open Access


    High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

    Yingying Li, Hang Jiang, Lianjie Yu, Jianfeng Li
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2123-2136, 2023, DOI:10.32604/cmes.2022.021407
    (This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract To improve the estimation accuracy, a novel time delay estimation (TDE) method based on the closed-form offset compensation is proposed. Firstly, we use the generalized cross-correlation with phase transform (GCC-PHAT) method to obtain the initial TDE. Secondly, a signal model using normalized cross spectrum is established, and the noise subspace is extracted by eigenvalue decomposition (EVD) of covariance matrix. Using the orthogonal relation between the steering vector and the noise subspace, the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE. Finally, the offsets are compensated via simple least squares (LS). Compared to other… More >

  • Open Access


    Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

    Jun Wu, Penghui Fan, Yingxin Sun, Weifeng Gui
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1305-1321, 2023, DOI:10.32604/cmes.2022.020919
    (This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract Based on the artificial intelligence algorithm of RetinaNet, we propose the Ghost-RetinaNet in this paper, a fast shadow detection method for photovoltaic panels, to solve the problems of extreme target density, large overlap, high cost and poor real-time performance in photovoltaic panel shadow detection. Firstly, the Ghost CSP module based on Cross Stage Partial (CSP) is adopted in feature extraction network to improve the accuracy and detection speed. Based on extracted features, recursive feature fusion structure is mentioned to enhance the feature information of all objects. We introduce the SiLU activation function and CIoU Loss to increase the learning and… More >

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