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Advanced Wireless Sensing Techniques for Communication

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 48712

Special Issue Editors


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Guest Editor
Department of Computer Science and Information Engineering, National Taipei University, No. 151, University Rd., San Shia District, New Taipei City 23741, Taiwan
Interests: wireless sensor networks; fog computing for sensors; software-defined sensors; sensors with 5G/6G; Internet of Things
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan City, Taiwan
Interests: medical image analysis; intelligent robots; Internet of Things; healthcare system; wireless and mobile communication network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of new advanced wireless sensing technologies for communication in smart cities with Internet of Things (IoT) has long been a particular concern for researchers. The key applications of smart cities with IoT technologies include smart economy, smart transportation, smart life, smart education, smart management, smart medical care, and smart environments. The recent COVID-19 pandemic has dealt a significant blow to the global economy and has had a major impact on global industries, education, health, and tourism. Therefore, how to more effectively combine new sensors and advanced communication technologies to effectively control COVID-19, and to further enhance the smart economy, smart transportation, smart life, smart education, and smart management in smart cities are very important topics. To support telemedicine and telehealth for smart medical care, wearable or non-invasive sensors combined with advanced wireless communications technology can effectively and continuously monitor patients to facilitate data collection and to immediately contact doctors when a temporary danger occurs in a long distance medicine environment. Furthermore, in the smart environment, environmental monitoring and sensing (pollution, food, and water quality, etc.) can be combined with advanced communication wireless sensing technology, such as effective collection of artificial intelligence data sets to perform artificial intelligence for environmental pollution analysis and monitoring to prevent health hazards to the general public.

This Special Issue aims to address advanced wireless sensing techniques for smart cities. We invite state-of-the-art theoretical, as well as practical works on a broad range of issues important to advanced wireless sensing techniques for researches, developers, and practitioners from both academia and industry.

Topics of primary interest include but are not limited to the following:

  • advanced wireless sensing techniques for Internet of Things in smart cities
  • advanced wireless sensing techniques for Internet of Nano Things with molecular communication (MC)
  • advanced wireless sensing techniques for 5G/beyond 5G/6G
  • advanced wireless sensing techniques for URLLC/eMTC/eMBB applications
  • advanced wireless sensing techniques with artificial intelligence
  • advanced wireless sensing techniques with security
  • advanced wireless sensing techniques with edge/cloud computing

Prof. Dr. Yuh-Shyan Chen
Prof. Dr. Ilsun You
Prof. Dr. Shih-Lin Wu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advanced wireless sensing techniques
  • 5G/beyond 5G/6G
  • artificial intelligence
  • security
  • edge/cloud computing

Published Papers (10 papers)

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Research

Jump to: Review

32 pages, 1912 KiB  
Article
A Modulated Wideband Converter Model Based on Linear Algebra and Its Application to Fast Calibration
by Gilles Burel, Anthony Fiche and Roland Gautier
Sensors 2022, 22(19), 7381; https://doi.org/10.3390/s22197381 - 28 Sep 2022
Cited by 1 | Viewed by 1276
Abstract
In the context of cognitive radio, smart cities and Internet-of-Things, the need for advanced radio spectrum monitoring becomes crucial. However, surveillance of a wide frequency band without using extremely expensive high sampling rate devices is a challenging task. The recent development of compressed [...] Read more.
In the context of cognitive radio, smart cities and Internet-of-Things, the need for advanced radio spectrum monitoring becomes crucial. However, surveillance of a wide frequency band without using extremely expensive high sampling rate devices is a challenging task. The recent development of compressed sampling approaches offers a promising solution to these problems. In this context, the Modulated Wideband Converter (MWC), a blind sub-Nyquist sampling system, is probably the most realistic approach and was successfully validated in real-world conditions. The MWC can be realized with existing analog components, and there exist calibration methods that are able to integrate the imperfections of the mixers, filters and ADCs, hence allowing its use in the real world. The MWC underlying model is based on signal processing concepts such as filtering, modulation, Fourier series decomposition, oversampling and undersampling, spectrum aliasing, and so on, as well as in-flow data processing. In this paper, we develop an MWC model that is entirely based on linear algebra, matrix theory and block processing. We show that this approach has many interests: straightforward translation of mathematical equations into simple and efficient software programming, suppression of some constraints of the initial model, and providing a basis for the development of an extremely fast system calibration method. With a typical MWC acquisition device, we obtained a speed-up of the calibration computation time by a factor greater than 20 compared with a previous implementation. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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35 pages, 6870 KiB  
Article
MiniDeep: A Standalone AI-Edge Platform with a Deep Learning-Based MINI-PC and AI-QSR System
by Yuh-Shyan Chen, Kuang-Hung Cheng, Chih-Shun Hsu and Hong-Lun Zhang
Sensors 2022, 22(16), 5975; https://doi.org/10.3390/s22165975 - 10 Aug 2022
Cited by 1 | Viewed by 2566
Abstract
In this paper, we present a new AI (Artificial Intelligence) edge platform, called “MiniDeep”, which provides a standalone deep learning platform based on the cloud-edge architecture. This AI-Edge platform provides developers with a whole deep learning development environment to set up their deep [...] Read more.
In this paper, we present a new AI (Artificial Intelligence) edge platform, called “MiniDeep”, which provides a standalone deep learning platform based on the cloud-edge architecture. This AI-Edge platform provides developers with a whole deep learning development environment to set up their deep learning life cycle processes, such as model training, model evaluation, model deployment, model inference, ground truth collecting, data pre-processing, and training data management. To the best of our knowledge, such a whole deep learning development environment has not been built before. MiniDeep uses Amazon Web Services (AWS) as the backend platform of a deep learning tuning management model. In the edge device, the OpenVino enables deep learning inference acceleration at the edge. To perform a deep learning life cycle job, MiniDeep proposes a mini deep life cycle (MDLC) system which is composed of several microservices from the cloud to the edge. MiniDeep provides Train Job Creator (TJC) for training dataset management and the models’ training schedule and Model Packager (MP) for model package management. All of them are based on several AWS cloud services. On the edge device, MiniDeep provides Inference Handler (IH) to handle deep learning inference by hosting RESTful API (Application Programming Interface) requests/responses from the end device. Data Provider (DP) is responsible for ground truth collection and dataset synchronization for the cloud. With the deep learning ability, this paper uses the MiniDeep platform to implement a recommendation system for AI-QSR (Quick Service Restaurant) KIOSK (interactive kiosk) application. AI-QSR uses the MiniDeep platform to train an LSTM (Long Short-Term Memory)-based recommendation system. The LSTM-based recommendation system converts KIOSK UI (User Interface) flow to the flow sequence and performs sequential recommendations with food suggestions. At the end of this paper, the efficiency of the proposed MiniDeep is verified through real experiments. The experiment results have demonstrated that the proposed LSTM-based scheme performs better than the rule-based scheme in terms of purchase hit accuracy, categorical cross-entropy, precision, recall, and F1 score. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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22 pages, 5767 KiB  
Article
JSQE: Joint Surveillance Quality and Energy Conservation for Barrier Coverage in WSNs
by Xuemei Shao, Chih-Yung Chang, Shenghui Zhao, Chin-Hwa Kuo, Diptendu Sinha Roy, Xinzhe Pi and Shin-Jer Yang
Sensors 2022, 22(11), 4120; https://doi.org/10.3390/s22114120 - 29 May 2022
Viewed by 1389
Abstract
Barrier coverage is a fundamental issue in wireless sensor networks (WSNs). Most existing works have developed centralized algorithms and applied the Boolean Sensing Model (BSM). However, the critical characteristics of sensors and environmental conditions have been neglected, which leads to the problem that [...] Read more.
Barrier coverage is a fundamental issue in wireless sensor networks (WSNs). Most existing works have developed centralized algorithms and applied the Boolean Sensing Model (BSM). However, the critical characteristics of sensors and environmental conditions have been neglected, which leads to the problem that the developed mechanisms are not practical, and their performance shows a large difference in real applications. On the other hand, the centralized algorithms also lack scalability and flexibility when the topologies of WSNs are dynamically changed. Based on the Elfes Sensing Model (ESM), this paper proposes a distributed Joint Surveillance Quality and Energy Conservation mechanism (JSQE), which aims to satisfy the requirements of the desired surveillance quality and minimize the number of working sensors. The proposed JSQE first evaluates the sensing probability of each sensor and identifies the location of the weakest surveillance quality. Then, the JSQE further schedules the sensor with the maximum contribution to the bottleneck location to improve the overall surveillance quality. Extensive experiment results show that our proposed JSQE outperforms the existing studies in terms of surveillance quality, the number of working sensors, and the efficiency and fairness of surveillance quality. In particular, the JSQE improves the surveillance quality by 15% and reduces the number of awake sensors by 22% compared with the relevant TOBA. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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25 pages, 4243 KiB  
Article
A Lightweight Passive Human Tracking Method Using Wi-Fi
by Jian Fang, Lei Wang, Zhenquan Qin, Bingxian Lu, Wenbo Zhao, Yixuan Hou and Jenhui Chen
Sensors 2022, 22(2), 541; https://doi.org/10.3390/s22020541 - 11 Jan 2022
Cited by 1 | Viewed by 2095
Abstract
Target tracking is a critical technique for localization in an indoor environment. Current target-tracking methods suffer from high overhead, high latency, and blind spots issues due to a large amount of data needing to be collected or trained. On the other hand, a [...] Read more.
Target tracking is a critical technique for localization in an indoor environment. Current target-tracking methods suffer from high overhead, high latency, and blind spots issues due to a large amount of data needing to be collected or trained. On the other hand, a lightweight tracking method is preferred in many cases instead of just pursuing accuracy. For this reason, in this paper, we propose a Wi-Fi-enabled Infrared-like Device-free (WIDE) method for target tracking to realize a lightweight target-tracking method. We first analyze the impact of target movement on the physical layer of the wireless link and establish a near real-time model between the Channel State Information (CSI) and human motion. Secondly, we make full use of the network structure formed by a large number of wireless devices already deployed in reality to achieve the goal. We validate the WIDE method in different environments. Extensive evaluation results show that the WIDE method is lightweight and can track targets rapidly as well as achieve satisfactory tracking results. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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24 pages, 5660 KiB  
Article
Mobile Charging Strategy for Wireless Rechargeable Sensor Networks
by Tzung-Shi Chen, Jen-Jee Chen, Xiang-You Gao and Tzung-Cheng Chen
Sensors 2022, 22(1), 359; https://doi.org/10.3390/s22010359 - 04 Jan 2022
Cited by 10 | Viewed by 2473
Abstract
In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of [...] Read more.
In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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39 pages, 4579 KiB  
Article
A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals
by Yuh-Shyan Chen, Yu-Chi Chang and Chun-Yu Li
Sensors 2021, 21(24), 8475; https://doi.org/10.3390/s21248475 - 19 Dec 2021
Viewed by 2609
Abstract
Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation [...] Read more.
Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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19 pages, 4341 KiB  
Article
Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition
by Aiguo Wang, Shenghui Zhao, Huan-Chao Keh, Guilin Chen and Diptendu Sinha Roy
Sensors 2021, 21(21), 6962; https://doi.org/10.3390/s21216962 - 20 Oct 2021
Cited by 1 | Viewed by 1729
Abstract
Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. [...] Read more.
Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. Accordingly, how to measure the relationships among activities and construct an activity recognizer for better distinguishing the confusing activities remains critical. To this end, we in this study propose a clustering guided hierarchical framework to discriminate on-going human activities. Specifically, we first introduce a clustering-based activity confusion index and exploit it to automatically and quantitatively measure the confusion between activities in a data-driven way instead of relying on the prior domain knowledge. Afterwards, we design a hierarchical activity recognition framework under the guidance of the confusion relationships to reduce the recognition errors between similar activities. Finally, the simulations on the benchmark datasets are evaluated and results show the superiority of the proposed model over its competitors. In addition, we experimentally evaluate the key components of the framework comprehensively, which indicates its flexibility and stability. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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22 pages, 4354 KiB  
Article
A Semi-Supervised Transfer Learning with Grid Segmentation for Outdoor Localization over LoRaWans
by Yuh-Shyan Chen, Chih-Shun Hsu and Chan-Yin Huang
Sensors 2021, 21(8), 2640; https://doi.org/10.3390/s21082640 - 09 Apr 2021
Cited by 4 | Viewed by 1890
Abstract
During the training phase of the supervised learning, it is not feasible to collect all the datasets of labelled data in an outdoor environment for the localization problem. The semi-supervised transfer learning is consequently used to pre-train a small number of labelled data [...] Read more.
During the training phase of the supervised learning, it is not feasible to collect all the datasets of labelled data in an outdoor environment for the localization problem. The semi-supervised transfer learning is consequently used to pre-train a small number of labelled data from the source domain to generate a kernel knowledge for the target domain. The kernel knowledge is transferred to a target domain to transfer some unlabelled data into the virtual labelled data. In this paper, we have proposed a new outdoor localization scheme using a semi-supervised transfer learning for LoRaWANs. In the proposed localization algorithm, a grid segmentation concept is proposed so as to generate a number of virtual labelled data through learning by constructing the relationship of labelled and unlabelled data. The labelled-unlabelled data relationship is repeatedly fine-tuned by correctly adding some more virtual labelled data. Basically, the more the virtual labelled data are added, the higher the location accuracy will be obtained. In the real implementation, three types of signal features, RSSI, SNR, and timestamps, are used for training to improve the location accuracy. The experimental results illustrate that the proposed scheme can improve the location accuracy and reduce the localization error as opposed to the existing outdoor localization schemes. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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Review

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25 pages, 4252 KiB  
Review
Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality
by Ananya Hazarika and Mehdi Rahmati
Sensors 2023, 23(7), 3682; https://doi.org/10.3390/s23073682 - 02 Apr 2023
Cited by 12 | Viewed by 6571
Abstract
Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of [...] Read more.
Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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32 pages, 2660 KiB  
Review
Study and Investigation on 5G Technology: A Systematic Review
by Ramraj Dangi, Praveen Lalwani, Gaurav Choudhary, Ilsun You and Giovanni Pau
Sensors 2022, 22(1), 26; https://doi.org/10.3390/s22010026 - 22 Dec 2021
Cited by 137 | Viewed by 23671
Abstract
In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile [...] Read more.
In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives. Full article
(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)
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