Intelligent Perception Computing and Graph Neural Networks: Algorithms, Applications, and New Challenges

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 1 March 2025 | Viewed by 1322

Special Issue Editors


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Guest Editor
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Interests: intelligent sensing; wireless perception; machine learning; blockchain

E-Mail Website
Guest Editor
Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0385, Japan
Interests: federated learning; poisoning attack; Internet of Things (IoT)

Special Issue Information

Dear Colleagues,

We invite you to submit your latest applied research in the field of intelligent perception and graph neural network algorithms to this Special Issue, entitled “Intelligent Perception Computing and Graph Neural Networks: Algorithms, Applications, and New Challenges”.

Intelligent perception has become increasingly crucial in various domains, including computer vision, sensor networks, and language education. Despite significant advancements, numerous challenges remain to be overcome in intelligent sensing, such as the handling of complex and heterogeneous data, efficiently processing large-scale information, and adapting to dynamic environments. In particular, wireless intelligent perception technology faces issues related to signal interference, resource allocation, and real-time processing.

Graph neural networks (GNNs) have emerged as a powerful tool for addressing these challenges in intelligent perception. By leveraging the inherent graph structure of data, GNNs can effectively capture the complex relationships and dependencies among entities. They have demonstrated superior performance in tasks such as object detection, scene comprehension, and signal classification. The ability of GNNs to model the intricate interactions between nodes and edges enables them to extract rich features and make accurate predictions, even in the presence of noise and uncertainty.

Despite the promising potential of GNNs in solving intelligent perception challenges, several open issues and limitations still need to be addressed. Graph neural networks often suffer from high computational complexity, making them difficult to deploy in resource-constrained scenarios. The efficiency of GNNs, both in terms of time and memory, is a critical concern for real-time applications. Moreover, the robustness of GNNs to adversarial attacks and data perturbations requires further investigation. Parallel computing techniques need to be explored to scale GNNs to large-scale datasets and enable their deployment in distributed systems.

To tackle these challenges and advance the state-of-the-art in graph neural networks for intelligent perception computing, we are organizing a Special Issue entitled "Intelligent Perception Computing and Graph Neural Networks: Algorithms, Applications, and New Challenges". We invite researchers and practitioners to submit original research articles, review papers, and case studies that explore novel algorithms, applications, and challenges in this field.

Topics of interest include, but are not limited to, the following:

  • Emerging GNN techniques for intelligent perception computing;
  • High-performance GNN-based algorithms for intelligent wireless perception technology;
  • Efficient and scalable GNN algorithms for large-scale data processing;
  • Robustness and adversarial defense mechanisms for GNNs;
  • Parallel and distributed computing techniques for GNNs;
  • GNN-based methods for sensor fusion and multi-modal data integration;
  • Applications of GNNs in computer vision, sensor networks, and wireless communication;
  • Theoretical analysis and understanding for intelligent perception scenarios, e.g., target localization, activity recognition, and posture estimation;
  • Benchmarking and evaluation frameworks for intelligent perception;
  • Solutions toward security, cryptography, and data privacy issues in intelligent perception;
  • New challenges and future directions in GNN-based intelligent perception computing.

We encourage submissions that showcase innovative ideas, rigorous methodologies, and significant practical impacts. All submitted papers will undergo a thorough peer-review process to ensure they are of the highest quality and relevance to this Special Issue’s theme.

Dr. Huakun Huang
Dr. Chunhua Su
Dr. Zhuotao Lian
Guest Editors

Manuscript Submission Information

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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. Mathematics 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

  • graph neural networks
  • intelligent perception computing
  • parallel and distributed computing
  • multi-modal data integration
  • computer vision
  • sensor networks
  • wireless communication
  • data privacy

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Published Papers (1 paper)

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Research

16 pages, 3374 KiB  
Article
P-CA: Privacy-Preserving Convolutional Autoencoder-Based Edge–Cloud Collaborative Computing for Human Behavior Recognition
by Haoda Wang, Chen Qiu, Chen Zhang, Jiantao Xu and Chunhua Su
Mathematics 2024, 12(16), 2587; https://doi.org/10.3390/math12162587 - 21 Aug 2024
Viewed by 838
Abstract
With the development of edge computing and deep learning, intelligent human behavior recognition has spawned extensive applications in smart worlds. However, current edge computing technology faces performance bottlenecks due to limited computing resources at the edge, which prevent deploying advanced deep neural networks. [...] Read more.
With the development of edge computing and deep learning, intelligent human behavior recognition has spawned extensive applications in smart worlds. However, current edge computing technology faces performance bottlenecks due to limited computing resources at the edge, which prevent deploying advanced deep neural networks. In addition, there is a risk of privacy leakage during interactions between the edge and the server. To tackle these problems, we propose an effective, privacy-preserving edge–cloud collaborative interaction scheme based on WiFi, named P-CA, for human behavior sensing. In our scheme, a convolutional autoencoder neural network is split into two parts. The shallow layers are deployed on the edge side for inference and privacy-preserving processing, while the deep layers are deployed on the server side to leverage its computing resources. Experimental results based on datasets collected from real testbeds demonstrate the effectiveness and considerable performance of the P-CA. The recognition accuracy can maintain 88%, although it could achieve about 94.8% without the mixing operation. In addition, the proposed P-CA achieves better recognition accuracy than two state-of-the-art methods, i.e., FedLoc and PPDFL, by 2.7% and 2.1%, respectively, while maintaining privacy. Full article
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