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

Submission Deadline: 28 February 2023 (closed)

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

Summary

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

    ARTICLE

    An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

    Jialun Lin, Qiong Chen
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1549-1561, 2024, DOI:10.32604/cmes.2023.029631
    (This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract Watermarks can provide reliable and secure copyright protection for optical coherence tomography (OCT) fundus images. The effective image segmentation is helpful for promoting OCT image watermarking. However, OCT images have a large amount of low-quality data, which seriously affects the performance of segmentation methods. Therefore, this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network (RCNN). First, the rough-set-based feature discretization module is designed to preprocess the input data. Second, a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select… More >

  • Open Access

    ARTICLE

    LSDA-APF: A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

    Xiaoli Li, Tongtong Jiao, Jinfeng Ma, Dongxing Duan, Shengbin Liang
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 595-617, 2024, DOI:10.32604/cmes.2023.029367
    (This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles… More >

    Graphic Abstract

    LSDA-APF: A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

  • Open Access

    ARTICLE

    Cooperative Rate Splitting Transmit Design for Full-Duplex-Enabled Multiple Multicast Communication Systems

    Siyi Duan, Mingsheng Wei, Shidang Li, Weiqiang Tan, Bencheng Yu
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 619-638, 2024, DOI:10.32604/cmes.2023.029572
    (This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract This paper examines the performance of Full-Duplex Cooperative Rate Splitting (FD-CRS) with Simultaneous Wireless Information and Power Transfer (SWIPT) support in Multiple Input Single Output (MISO) networks. In a Rate Splitting Multiple Access (RSMA) multicast system with two local users and one remote user, the common data stream contains the needs of all users, and all users can decode the common data stream. Therefore, each user can receive some information that other users need, and local users with better channel conditions can use this information to further enhance the reception reliability and data rate of users with poor channel quality.… More >

  • Open Access

    ARTICLE

    Outage Probability Analysis for D2D-Enabled Heterogeneous Cellular Networks with Exclusion Zone: A Stochastic Geometry Approach

    Yulei Wang, Li Feng, Shumin Yao, Hong Liang, Haoxu Shi, Yuqiang Chen
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 639-661, 2024, DOI:10.32604/cmes.2023.029565
    (This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract Interference management is one of the most important issues in the device-to-device (D2D)-enabled heterogeneous cellular networks (HetCNets) due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum. To alleviate the interference, an efficient interference management way is to set exclusion zones around the cellular receivers. In this paper, we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets. The main difficulties contain three aspects: 1) how to model the location randomness of base stations, cellular and D2D users in practical networks; 2)… More >

  • Open Access

    ARTICLE

    Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

    Lilan Zou, Bo Liang, Xu Cheng, Shufa Li, Cong Lin
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2641-2659, 2023, DOI:10.32604/cmes.2023.028037
    (This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment. In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment, we proposed a more effective and robust target detection framework based on deep learning, which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection. Firstly, the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with… More >

    Graphic Abstract

    Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

  • Open Access

    ARTICLE

    QBFO-BOMP Based Channel Estimation Algorithm for mmWave Massive MIMO Systems

    Xiaoli Jing, Xianpeng Wang, Xiang Lan, Ting Su
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1789-1804, 2023, DOI:10.32604/cmes.2023.028477
    (This article belongs to this Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
    Abstract At present, the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity. The bacterial foraging optimization (BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability. But the BFO-based algorithm is easy to fall into local optimum. Therefore, this paper proposes the quantum bacterial foraging optimization (QBFO)-binary orthogonal matching pursuit (BOMP) channel estimation algorithm to the problem of local optimization. Firstly, the binary matrix is constructed according to whether atoms are selected or not. And the support set of the sparse signal is recovered… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    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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