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Artificial Intelligence and Computing in IoT-Based Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 741

Special Issue Editor


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Guest Editor
College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
Interests: edge computing; blockchain; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, there has been remarkable development in artificial intelligence (AI), particularly in IoT-based applications. The availability of abundant AI computing power is a critical driver behind the proliferation of AI applications across different domains. The rapidly advancing AI computing technology and frameworks, characterized by heterogeneity, acceleration, and programmability, have created numerous opportunities for meaningful research and practical applications of AI.

Indeed, there is a long way to go before the computing-networking amalgamation system reaches maturity, and several challenges need to be addressed. Firstly, providing users with adaptable AI computing services to cater to their diverse requirements is crucial. The insights derived from extensive neural network (NN) training lead to a surging demand for AI computing power. Secondly, supporting flexible networking services is essential for achieving rapid response times. The collaboration between various end devices, edge nodes, and clouds in delivering AI services can lead to network congestion and low energy efficiency. Thirdly, ensuring the welfare of computing power providers is vital to maximize the value of the computing-networking system. The incentive mechanism and business model play a pivotal role in driving widespread adoption. Therefore, deep consideration is required to understand the motivations behind assisting others in training their NNs by contributing one's own computing power.

This featured topic seeks comprehensive discussions on innovative functionalities and technologies, including protocols, across a diverse range of computing and networking equipment. Examples of such equipment include base stations, femtocells/small cells, mobile phones, Wi-Fi routers, and edge computing servers. The topic encompasses various perspectives, such as amalgamated algorithms, allocation schemes, incentive modeling, and optimization. The goal is to offer tutorial information, share recent research outcomes, review economic opportunities, address technical challenges, explore potential regulatory solutions, and identify emerging trends. Through this feature topic, we aim to promote a deeper understanding of the advancements in computing and networking domains, fostering collaboration and knowledge dissemination among researchers and practitioners.

Dr. Chao Qiu
Guest Editor

Manuscript Submission Information

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Keywords

  • AI
  • computing-networking amalgamation system
  • AI computing services
  • IoT-based applications

Published Papers (1 paper)

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Research

0 pages, 439 KiB  
Communication
Sensor-Based Indoor Fire Forecasting Using Transformer Encoder
by Young-Seob Jeong, JunHa Hwang, SeungDong Lee, Goodwill Erasmo Ndomba, Youngjin Kim and Jeung-Im Kim
Sensors 2024, 24(7), 2379; https://doi.org/10.3390/s24072379 - 8 Apr 2024
Viewed by 483
Abstract
Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. [...] Read more.
Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computing in IoT-Based Applications)
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