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Selected Papers from the 1st International Workshop on Technology of AI and Wireless Advanced Networking: Dependable Computing and Communication (TAIWAN-DCC) in Conjunction with the 16th European Dependable Computing Conference (EDCC 2020)

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 2821

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Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
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Special Issue Information

Dear Colleagues,

This Special Issue is organized to discuss the state-of-the-art and emerging research, applications, and problems in the technology of Artificial Intelligence and Wireless Advanced Networking fields. The explosive data growth from wireless and advanced networks can be highly unstructured, heterogeneous, and unpredictable. AI techniques have been applied in almost every domain. The security issues, such as network intrusion detection problems in the IoT, ITS, and wireless advanced networking system, have gained significant concerns. Also, the emerging technologies and issues in the areas of ubiquitous services, wireless and multimedia applications and networking issues for 5G/Beyond 5G are all within the scope of this Special Issue. This Special Issue will focus on the prospective technologies, models, systems and applications in AI, IoTs, ITS, 5G and advanced networking areas. The aim is to collect the most recent advances in Artificial Intelligence research for Wireless Advanced Networking fields. Accordingly, the Special Issue welcomes methods and ideas that emphasize the impact of Artificial Intelligence on Wireless Advanced Networking technologies. The topics of interest for this Special Issue include, but are not limited to: Artificial Intelligence (AI), Machine Learning and Deep Learning; Innovative AI incentive schemes, Distributed AI algorithms and techniques; 5G and wireless advanced networking; Intelligent transportation systems (ITS); Ultra-reliable low-latency communication; Internet of Things (IoTs) and Wireless Sensor Networks (WSNs); Software defined networking (SDN) technology, system, and architecture; Cloud, fog, and mobile edge computing systems; Fault-tolerant issues in advanced networking; Security for IoT, WSN, SDN, 5G, and AI applications; QoS/QoE performance evaluation; Dependable and secure computing

Prof. Dr. Yung-Fa Huang
Guest Editor

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Keywords

  • Wireless Sensor Networks
  • Artificial Intelligence
  • 5G
  • Internet of Things

Published Papers (1 paper)

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Research

16 pages, 7534 KiB  
Article
Using High-Frequency Information and RH to Estimate AQI Based on SVR
by Jiun-Jian Liaw and Kuan-Yu Chen
Sensors 2021, 21(11), 3630; https://doi.org/10.3390/s21113630 - 23 May 2021
Cited by 7 | Viewed by 2207
Abstract
The Environmental Protection Administration of Taiwan’s Executive Yuan has set up many air quality monitoring stations to monitor air pollution in the environment. The current weather forecast also includes information used to predict air pollution. Since air quality indicators have a considerable impact [...] Read more.
The Environmental Protection Administration of Taiwan’s Executive Yuan has set up many air quality monitoring stations to monitor air pollution in the environment. The current weather forecast also includes information used to predict air pollution. Since air quality indicators have a considerable impact on people, the development of a simple, fast, and low-cost method to measure the AQI value is a worthy topic of research. In this study, a method was proposed to estimate AQI. Visibility had a clear positive relationship with AQI. When images and AQI were compared, it was easy to see that visibility decreased with the AQI value increase. Distance is the main factor affecting visibility, so measuring visibility with images has also become a research topic. Images with high and low PM2.5 concentrations were used to obtain regions of interest (RoI). The pixels in the RoI were calculated to obtain high-frequency information. The high-frequency information of RoI, RH, and true AQI was used for training via SVR, which was used to generate the model for AQI estimation. One year of experimental samples was collected for the experiment. Two indices were used to evaluate the performance of the proposed method. The results showed that the proposed method could be used to estimate AQI with acceptable performance in a simple, fast, and low-cost way. Full article
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