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Wi-Fi Sensing: Applications and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (25 December 2021) | Viewed by 2835

Special Issue Editor


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Guest Editor
School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea
Interests: wireless networks and mobile computing; Internet-of-Things (IoT); computer security: network system management/secure monitoring; AI-based Wi-Fi sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, Wi-Fi sensing is attracting increasing attention as a new innovative sensing technology. Wi-Fi sensing is a technology which uses exiting Wi-Fi signals to perform sensing applications such as motion detection and gesture recognition as well as fine-grained biometric measurement. Compared with traditional approaches, it has a series of advantages and provides a compelling alternative, for instance, in that it does not require lighting, offers better coverage as it can sense through walls, and offers a high level of user privacy. As a result, it provides opportunities for home security, healthcare and service providers within enterprises and many more. This growth in Wi-Fi sensing applications brings forward an inevitable need for more efficient and intelligent processing, implementation and deployment.

While some applications can be enabled using existing standards, however, there is no standard for Wi-Fi sensing and its implementation, limiting the range of what Wi-Fi sensing can do. Consequently, new tools and methods are required to embrace a variety of Wi-Fi sensing applications.

The aim of this Special Issue is to investigate the latest research trends and recent development of new frameworks, mechanisms, and algorithms that are able to support Wi-Fi sensing technology.

Prof. Dr. Jaehyuk Choi
Guest Editor

Manuscript Submission Information

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Keywords

  • wireless sensing
  • Wi-Fi Sensing
  • through the wall sensing
  • mmWave Sensing
  • device-free human behavior recognition
  • motion detection
  • activity recognition
  • human identification
  • tracking
  • Channel State Information
  • indoor localization
  • elderly people monitoring
  • multi-person detection
  • deep learning classification
  • signal pre-processing
  • 802.11bf
  • people counting

Published Papers (1 paper)

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Research

16 pages, 2298 KiB  
Article
WiFi Positioning in 3GPP Indoor Office with Modified Particle Swarm Optimization
by Sung Hyun Oh and Jeong Gon Kim
Appl. Sci. 2021, 11(20), 9522; https://doi.org/10.3390/app11209522 - 13 Oct 2021
Cited by 5 | Viewed by 1675
Abstract
With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. [...] Read more.
With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. In addition, smartphones, which are IoT devices, are owned by most people. Based on these advantages, the above three technologies can be combined and effectively applied to navigation technology. In the case of an outdoor environment, global positioning system (GPS) technology has been developed to enable relatively accurate positioning of the user. However, due to the problem of radio wave loss because of many obstacles and walls, there are obvious limitations in applying GPS to indoor environments. Hence, we propose a method to increase the accuracy of user positioning in indoor environments using wireless-fidelity (Wi-Fi). The core technology of the proposed method is to limit the initial search region of the particle swarm optimization (PSO), an intelligent particle algorithm; doing so increases the probability that particles converge to the global optimum and shortens the convergence time of the algorithm. For this reason, the proposed method can achieve fast processing time and high accuracy. To limit the initial search region of the PSO, we first build an received signal strength indicator (RSSI) database for each sample point (SP) using a fingerprinting scheme. Then, a limited region is established through a fuzzy matching algorithm. Finally, the particles are randomly distributed within a limited region, and then the user’s location is positioned through a PSO. Simulation results confirm that the method proposed in this paper achieves the highest positioning accuracy, with an error of about 1 m when the SP interval is 3 m in an indoor environment. Full article
(This article belongs to the Special Issue Wi-Fi Sensing: Applications and Challenges)
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