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Electronics
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Artificial Intelligence for IoT Systems
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Artificial Intelligence for IoT Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 16 July 2024 | Viewed by 873

Special Issue Editors

Dr. Xu Zhang

E-Mail Website
Guest Editor
Faculty of Engineering and Physical Sciences, University of Leeds, Leeds LS2 9JT, UK
Interests: AI for computing and networking; Internet of Things; multimedia networking
Special Issues, Collections and Topics in MDPI journals
Dr. Wang Miao

E-Mail Website
Guest Editor
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
Interests: mobile edge computing; software-defined networking; network function virtualization; AI/ML-driven resource optimization; performance modeling and analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Jia Hu

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter, Exeter EX4 4PY, UK
Interests: edge–cloud computing; resource optimization; applied machine learning; network security
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Z. Hao

E-Mail Website
Guest Editor
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
Interests: AI for robotic application; AI for space exploration; LLM for robotics
Dr. Ke Li

E-Mail Website
Guest Editor
School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
Interests: vehicular networks; intelligent transportation systems; Internet of Things, machine learning; cloud data center networks
Dr. Yining Hua

E-Mail Website
Guest Editor
School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK
Interests: applied artificial intelligence; robotics & autonomous systems; future generation computer networks; Internet of things; distributed wireless systems; information security; blockchain
Dr. Ting Li

E-Mail Website
Guest Editor
The School of Computer, Electronics and Information, Guangxi University, Nanning 530005, China
Interests: network optimization; mobile edge computing (MEC); privacy protection

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) encompasses a network of interconnected physical objects, devices, and sensors that gather and exchange data through the internet. It has achieved great success across various industries and in everyday life, such as smart homes, healthcare, agriculture, transportation, retail, and smart cities. The application of AI in these systems is essential for processing and acting on the vast data generated by IoT devices, enabling real-time insights, automation, and personalized experiences to ultimately maximize their potential applications. It can enhance security in IoT systems, ensure scalability, and open doors to natural language interaction; this combination is essential for unlocking the full potential of the Internet of Things in various applications and industries. However, existing AI for IoT faces limitations in terms of computational resources, energy efficiency, security vulnerabilities, and the ability to handle real-time data processing at scale.

By soliciting original, previously unpublished, empirical, experimental, and theoretical research works at the intersection of IoT, AI, cloud/edge computing, and network technologies, this Special Issue aims to bring together researchers in these fields and address new challenges in AI for IoT networks. Research areas may include (but are not limited to) the following:

  • Artificial intelligence;
  • Internet of Things;
  • Edge computing/intelligence;
  • Artificial Intelligence of Things (AIoT);
  • Deep learning;
  • Deep reinforcement learning;
  • Federated learning;
  • Data collection and data mining;
  • Resource management;
  • IoT security/privacy.

We look forward to receiving your contributions.

Dr. Xu Zhang
Dr. Wang Miao
Dr. Jia Hu
Dr. Daniel Z. Hao
Dr. Ke Li
Dr. Yining Hua
Dr. Ting Li
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. Electronics 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 2400 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

  • artificial intelligence
  • internet of things
  • edge computing
  • edge intelligence
  • artificial intelligence of things (AIoT)
  • deep learning
  • deep reinforcement learning
  • federated learning
  • data collection and data mining
  • resource management

Published Papers (1 paper)

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Research

15 pages, 2048 KiB  
Article
MGAD: Mutual Information and Graph Embedding Based Anomaly Detection in Multivariate Time Series
by Yuehua Huang, Wenfen Liu, Song Li, Ying Guo and Wen Chen
Electronics 2024, 13(7), 1326; https://doi.org/10.3390/electronics13071326 - 01 Apr 2024
Viewed by 607
Abstract
Along with the popularity of mobile Internet and smart applications, more and more high-dimensional sensor data have appeared, and these high-dimensional sensor data have hidden information about system performance degradation, system failure, etc., and how to mine them to obtain such information is [...] Read more.
Along with the popularity of mobile Internet and smart applications, more and more high-dimensional sensor data have appeared, and these high-dimensional sensor data have hidden information about system performance degradation, system failure, etc., and how to mine them to obtain such information is a very difficult problem. This challenge can be solved by anomaly detection techniques, which is an important field of research in data mining, especially in the domains of network security, credit card fraud detection, industrial fault identification, etc. However, there are many difficulties in anomaly detection in multivariate time-series data, including poor accuracy, fast data generation, lack of labeled data, and how to capture information between sensors. To address these issues, we present a mutual information and graph embedding based anomaly detection algorithm in multivariate time series, called MGAD (mutual information and graph embedding based anomaly detection). The MGAD algorithm consists of four steps: (1) Embedding of sensor data, where heterogeneous sensor data become different vectors in the same vector space; (2) Constructing a relationship graph between sensors using their mutual information about each other; (3) Learning the relationship graph between sensors using a graph attention mechanism, to predict the sensor data at the next moment; (4) Compare the predicted values with the real sensor data to detect potential outliers. Our contributions are as follows: (1) we propose an unsupervised outlier detection called MGAD with a high interpretability and accuracy; (2) massive experiments on benchmark datasets have demonstrated the superior performance of the MGAD algorithm, compared with state-of-the-art baselines in terms of ROC, F1, and AP. Full article
(This article belongs to the Special Issue Artificial Intelligence for IoT Systems)
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