Empowering IoT with AI: AIoT for Smart and Autonomous Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 852

Special Issue Editors


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Guest Editor
College of Artificial Intelligence, Dalian Maritime University, Dalian 116026, China
Interests: computer networking; edge intelligence; federated learning

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Guest Editor
Information Systems Technology and Design, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore 487372, Singapore
Interests: network intelligence; federated learning; machine learning

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Guest Editor
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Interests: integrated air–ground networks, UAV networks, wireless energy transfer, and optimization

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Guest Editor
Information Systems Technology and Design, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore 487372, Singapore
Interests: digital twin; UAV communication and networking; open-RAN; deep reinforcement learning

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is revolutionizing daily life, but artificial intelligence (AI) truly unlocks its full potential. Three pivotal emerging technologies—AI, 5G networks, and big data—are enhancing IoT, culminating in the Artificial Intelligence of Things (AIoT). The primary objective of AIoT is to bring intelligence to the edge, enabling devices to recognize data, evaluate their environments, and determine optimal actions. Through AI, IoT devices have evolved into sophisticated machines capable of autonomous analytics and independent decision making, far surpassing their initial roles as mere data transmitters.

Nonetheless, the integration of IoT and AI in the context of smart and autonomous systems introduces critical challenges for the research community. A crucial step in realizing the full potential of AIoT is fostering collaboration among massive distributed devices. Without such collaboration, AIoT systems may encounter issues such as inefficient energy use, security vulnerabilities, and inconsistent performance. Another essential aspect is the seamless integration of AIoT with other cutting-edge technologies. Both academia and industry must prioritize bridging the gap between AIoT with 5G networks, edge computing, blockchain, digital twin, and other emerging technologies to achieve comprehensive convergence.

This Special Issue seeks high-quality contributions from experts in academia and industry in the fields of AIoT, machine learning, 5G networks, and big data. We solicit high-quality original papers on the development of AI models for IoT systems and the presentation of pioneering approaches and applications.

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

  • Novel AIoT architectures and frameworks;
  • Integration and application of 5G networks with AIoT;
  • Security and privacy solutions for AIoT against malicious attacks;
  • Intelligent edge/fog/cloud computing;
  • Integration of large language model (LLM)/large vision model (LVM) and AIoT;
  • AIoT-Enabled Semantic Communication;
  • Distributed Collaborative Learning in AioT;
  • Robust distributed AIoT design in smart and autonomous system;
  • Interplay between digital twin and AIoT over networks;
  • Novel methods for AIoT solutions with limited communication/computation resources;
  • AI-driven applications in smart homes, smart cities, and smart industries.

We look forward to receiving your contributions.

Dr. Zijian Li
Dr. Zihan Chen
Dr. Jiahui Li
Dr. Longyu Zhou
Guest Editors

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Keywords

  • artificial intelligence of things
  • edge computing
  • 5G networks
  • security and privacy
  • AI-driven applications

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Published Papers (1 paper)

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Research

15 pages, 1402 KiB  
Article
Enhancing Anomaly Detection in Maritime Operational IoT Time Series Data with Synthetic Outliers
by Hyunjoo Kim and Inwhee Joe
Electronics 2024, 13(19), 3912; https://doi.org/10.3390/electronics13193912 - 3 Oct 2024
Viewed by 583
Abstract
Detecting anomalies in engine and machinery data during ship operations is crucial for maintaining the safety and efficiency of the vessel. We conducted experiments using device data from the maritime industry, consisting of time series records from IoT (Internet of Things) datasets such [...] Read more.
Detecting anomalies in engine and machinery data during ship operations is crucial for maintaining the safety and efficiency of the vessel. We conducted experiments using device data from the maritime industry, consisting of time series records from IoT (Internet of Things) datasets such as cylinder and exhaust gas temperatures, coolant temperatures, and cylinder pressures collected from various sensors on the ship’s equipment. We propose data enrichment and validation techniques by generating synthetic outliers through data degradation and data augmentation with a Transformer backbone, utilizing the maritime operational data. We extract a portion of the input data and replace it with synthetic outliers. The created anomaly data are then used to train the model via a self-supervised learning approach. Synthetic outliers are generated using methods such as the arithmetic mean, geometric mean, median, local scale, global scale, and magnitude warping. With our methodology, we achieved a 17.23% improvement in F1 performance compared to existing state-of-the-art methods across five publicly available datasets and actual maritime operational data collected from the industry. Full article
(This article belongs to the Special Issue Empowering IoT with AI: AIoT for Smart and Autonomous Systems)
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