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The advent of artificial intelligence (AI) and the Internet of Things (IoT) in today's digital world has significantly changed the way people work, entertain, and live. The number of IoT devices connected to various networks has grown exponentially. Data Centers estimates that there will be more than 41 billion connected devices in use by 2025. The amount of information being sent back to the cloud grows in proportion to the number of connected devices. Today's data centers struggle to provide guaranteed transfer rates and response times due to the massive amounts of data that must be processed in the cloud.

Edge computing is a distributed computing model that allows computing tasks to be processed on edge nodes closest to the data source, reducing latency and network traffic. IoT devices at the edge are assisted with the latest generation of AI application deployments for use cases such as predictive maintenance of medical precision equipment, industrial quality inspection, and others. Concurrently, AI is developed to supply local scene linking, local data security management, and high real-time business response, all in line with the actual needs of IoT edge computing.

In this environment, generative AI technology can perform model training and inference directly on edge devices, improving computational efficiency and data security. Meanwhile, intelligent devices and sensors in the IoT continue to generate various types of data that can be collected and analyzed to support decision-making and business optimization. Through generative AI technology, people can more accurately predict future events and trends, thereby improving the intelligence level and production efficiency of intelligent devices. Therefore, generative AI technology is closely related to edge computing and IoT and can strongly support the development of these fields. In this context, generative AI can use the deep learning data generated in the cloud to perform model inference and prediction on the source of data from IoT devices at the edge. Smarter IoT devices are now possible with the help of generative AI. In a nutshell, generative AI at the edge in modern IoT can bring many benefits: data is more secure, consumes less power, has a shorter delay, is more reliable, can make better use of data, and has lower data processing costs.

The convergence of generative AI and IoT applications is a trend with great potential. Edge computing-based devices in the IoT can autonomously improve their performance in a given task through data learning, often surpassing human capabilities. Based on this, this Special Issue (SI) is primarily aimed at researchers in the fields of AI and computer science to solicit papers on the applications of generative AI in IoT and to promote the growth of IoT. Topics applicable to this special issue include, but are not limited to:

• Patient Monitoring for Medical IoT Based on Generative AI at the edge.
• Security Risk Concerns of Generative AI in the Modern IoT.
• Application of IoT Devices Based on Generative AI at the edge in Market Forecasting and Recommendation.
• Application of Generative AI at the edge in Modern Internet of Vehicles Traffic Monitoring.
• Generative AI at the edge Technology for Vehicle Classification and Violation Detection on the Internet of Vehicles.
• Modern IoT Speech Recognition and Natural Language Processing for Generative AI at the edge Technology.
• Generative AI in Modern IoT Logistics Sorting.
• Generative AI in Modern IoT Smart Homes.
• Generative AI for Explosive Data Processing in the Modern Industrial IoT.
• Smart Virtual Assistant for Modern IoT Retail Industry Based on Generative AI at the edge.

Submission Guidelines

Prospective authors should submit their manuscripts following the IEEE IoT Magazine guidelines. Authors should submit a manuscript through Manuscript Central.

Important Dates

Manuscript Submission Deadline: 15 February 2024 (Extended Deadline)
Author Notification: 30 December 2023
Revised Manuscript Due: 29 February 2024
Notification of Acceptance: 15 April 2024
Publication Date: Second Quarter 2024

Guest Editors

Zhihan Lv (Lead Guest Editor)
Uppsala University, Sweden

James J. Park
Seoul National University of Science and Technology, South Korea

Jun Shen
University of Wollongong, Australia

Houbing Song
University of Maryland, Baltimore County (UMBC), USA

Yi Zhang (Secondary Corresponding Guest Editor)
Intel Labs, USA