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Radio Sensing and Sensor Networks
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Radio Sensing and Sensor Networks

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

Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 27259

Special Issue Editors

Dr. Stefano Savazzi

E-Mail Website
Guest Editor
Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Consiglio Nazionale delle Ricerche (CNR), P.zza L. da Vinci 32, I-20133 Milan, Italy
Interests: signal processing aspects of wireless communications systems; antenna array processing; channel estimation and tracking; MIMO-OFDM systems; cooperative communication; ad-hoc networking and wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Sanaz Kianoush

E-Mail Website
Guest Editor
Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Consiglio Nazionale delle Ricerche (CNR), c/o, Politecnico di Milano, P.zza L. da Vinci 32, I-20133 Milano, Italy
Interests: device-free radio localization and activity recognition; signal processing and machine learning in wireless systems
Special Issues, Collections and Topics in MDPI journals
Dr. Vittorio Rampa

E-Mail Website
Guest Editor
Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Consiglio Nazionale delle Ricerche (CNR), c/o, Politecnico di Milano, P.zza L. da Vinci 32, I-20133 Milano, Italy
Interests: software-defined radios; device-free radio localization and activity recognition; body models for device-free localization; signal processing and machine learning for communication systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, the development of techniques to capture and process wireless stray electromagnetic (EM) radiation from different radio sources is gaining increasing attention. In particular, these techniques can be exploited to transform radio networks into virtual radio sensors to allow advance human-scale sensing, human behavior recognition, detection/localization, and crowd density estimation/mapping. Passive, or device-free, radio sensing is, for example, an emerging paradigm that transforms existing wireless networks by adding sensing modalities to improve the perception of users and the environment. In the context of real-time human-scale sensing, no single technology can currently solve the problem of continuous people monitoring in different situations and scenarios, while the combination, transformation, and hybridization of multiple sensing and networking technologies are key enablers to achieve accurate human sensing. 

This Special Issue encourages authors from academia and industry to submit manuscripts on innovations on radio sensing, networks, and computing techniques for human scale sensing. The Special Issue topics include, but are not limited to

  • Active (wearable-based) and passive (device-free) radio sensing systems;
  • Location-based services, motion detection, tracking, and perception;
  • Indoor navigation, mapping, and channel charting;
  • EM body motion modelling for radio sensing;
  • Mm-Wave and THz technologies for passive and active radio sensing;
  • Free-space user interfaces and human–machine interactions;
  • Applications of radio sensing in human–robot collaboration workplaces, smart living and Industry 4.0 scenarios;
  • Beyond 5G technologies for radio sensing and sensor networks;
  • Multi-spectral and multi-domain data-fusion techniques to improve the robustness of radio sensing systems

Dr. Stefano Savazzi
Dr. Sanaz Kianoush
Dr. Vittorio Rampa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • radio sensing
  • localization and perception
  • sensor networks for device-free and wearable-based sensing
  • transformative computing
  • mm-wave and thz radios

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Published Papers (6 papers)

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Research

22 pages, 2706 KiB  
Article
EM Model-Based Device-Free Localization of Multiple Bodies
by Vittorio Rampa, Monica Nicoli, Chiara Manno and Stefano Savazzi
Sensors 2021, 21(5), 1728; https://doi.org/10.3390/s21051728 - 3 Mar 2021
Cited by 9 | Viewed by 2171
Abstract
In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless network. The presence and motion of non-instrumented subjects leave a specific footprint on the received Radio-Frequency (RF) signals by affecting [...] Read more.
In this paper, we discuss the problem of device-free localization and tracking, considering multiple bodies moving inside an area monitored by a wireless network. The presence and motion of non-instrumented subjects leave a specific footprint on the received Radio-Frequency (RF) signals by affecting the Received Signal Strength (RSS) in a way that strongly depends on people location. The paper targets specifically the modelling of the effects on the electromagnetic (EM) field, and the related inference methods. A multiple-body diffraction model is exploited to predict the impact of these bodies on the RSS field, i.e., the multi-body-induced shadowing, in the form of an extra attenuation w.r.t. the reference scenario where no targets are inside the monitored area. Unlike almost all methods available in the literature, that assume multi-body-induced shadowing to sum linearly with the number of people co-present in the monitored area, the proposed model describes also the EM effects caused by their mutual interactions. As a relevant case study, the proposed EM model is exploited to predict and evaluate the effects due to two co-located bodies inside the monitored area. The proposed real-time localization and tracking method, exploiting both average and deviation of the RSS perturbations due to the two subjects, is compared against others techniques available in the literature. Finally, some results, based on experimental RF data collected in a representative indoor environment, are presented and discussed. Full article
(This article belongs to the Special Issue Radio Sensing and Sensor Networks)
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23 pages, 5911 KiB  
Article
A Robust Localization Algorithm Based on NLOS Identification and Classification Filtering for Wireless Sensor Network
by Long Cheng, Sihang Huang, Mingkun Xue and Yangyang Bi
Sensors 2020, 20(22), 6634; https://doi.org/10.3390/s20226634 - 19 Nov 2020
Cited by 8 | Viewed by 2863
Abstract
With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In [...] Read more.
With the rapid development of information and communication technology, the wireless sensor network (WSN) has shown broad application prospects in a growing number of fields. The non-line-of-sight (NLOS) problem is the main challenge to WSN localization, which seriously reduces the positioning accuracy. In this paper, a robust localization algorithm based on NLOS identification and classification filtering for WSN is proposed to solve this problem. It is difficult to use a single filter to filter out NLOS noise in all cases since NLOS cases are extremely complicated in real scenarios. Therefore, in order to improve the robustness, we first propose a NLOS identification strategy to detect the severity of NLOS, and then NLOS situations are divided into two categories according to the severity: mild NLOS and severe NLOS. Secondly, classification filtering is performed to obtain respective position estimates. An extended Kalman filter is applied to filter line-of-sight (LOS) noise. For mild NLOS, the large outliers are clipped by the redescending score function in the robust extended Kalman filter, yielding superior performance. For severe NLOS, a severe NLOS mitigation algorithm based on LOS reconstruction is proposed, in which the average value of NLOS error is estimated and the measurements are reconstructed and corrected for subsequent positioning. Finally, an interactive multiple model algorithm is employed to obtain the final positioning result by weighting the position estimation of LOS and NLOS. Simulation and experimental results show that the proposed algorithm can effectively suppress NLOS error and obtain higher positioning accuracy when compared with existing algorithms. Full article
(This article belongs to the Special Issue Radio Sensing and Sensor Networks)
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19 pages, 4538 KiB  
Article
Dwell Time Allocation Algorithm for Multiple Target Tracking in LPI Radar Network Based on Cooperative Game
by Chenyan Xue, Ling Wang and Daiyin Zhu
Sensors 2020, 20(20), 5944; https://doi.org/10.3390/s20205944 - 21 Oct 2020
Viewed by 2699
Abstract
To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low [...] Read more.
To solve the problem of dwell time management for multiple target tracking in Low Probability of Intercept (LPI) radar network, a Nash bargaining solution (NBS) dwell time allocation algorithm based on cooperative game theory is proposed. This algorithm can achieve the desired low interception performance by optimizing the allocation of the dwell time of each radar under the constraints of the given target detection performance, minimizing the total dwell time of radar network. By introducing two variables, dwell time and target allocation indicators, we decompose the dwell time and target allocation into two subproblems. Firstly, combining the Lagrange relaxation algorithm with the Newton iteration method, we derive the iterative formula for the dwell time of each radar. The dwell time allocation of the radars corresponding to each target is obtained. Secondly, we use the fixed Hungarian algorithm to determine the target allocation scheme based on the dwell time allocation results. Simulation results show that the proposed algorithm can effectively reduce the total dwell time of the radar network, and hence, improve the LPI performance. Full article
(This article belongs to the Special Issue Radio Sensing and Sensor Networks)
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32 pages, 3503 KiB  
Article
Large-Scale Crowd Analysis through the Use of Passive Radio Sensing Networks
by Stijn Denis, Ben Bellekens, Abdil Kaya, Rafael Berkvens and Maarten Weyn
Sensors 2020, 20(9), 2624; https://doi.org/10.3390/s20092624 - 4 May 2020
Cited by 11 | Viewed by 3780
Abstract
The creation of an automatic crowd estimation system capable of providing reliable, real-time estimates of human crowd sizes would be an invaluable tool for organizers of large-scale events, particularly so in the context of safety management. We describe a set of experiments in [...] Read more.
The creation of an automatic crowd estimation system capable of providing reliable, real-time estimates of human crowd sizes would be an invaluable tool for organizers of large-scale events, particularly so in the context of safety management. We describe a set of experiments in which we installed a passive Radio Frequency (RF) sensor network in different environments containing thousands of human individuals and discuss the accuracy with which the resulting measurements can be used to estimate the sizes of these crowds. Depending on the selected training approach, a median crowd estimation error of 184 people could be obtained for a large scale environment which contained 3227 people at its peak. Additionally, we look into the potential benefits of dividing one of our experimental environments into multiple subregions and open up a potentially interesting new topic of research regarding the estimation of crowd flows. Finally, we investigate the combination of our measurements with another sources of crowd-related data: sales data from drink stands within the environment. In doing so, we aim to integrate the concept of an automatic RF-based crowd estimation system into the broader domain of crowd analysis. Full article
(This article belongs to the Special Issue Radio Sensing and Sensor Networks)
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20 pages, 2997 KiB  
Article
A Multi-Static Radar Network with Ultra-Wideband Radio-Equipped Devices
by Anton Ledergerber and Raffaello D’Andrea
Sensors 2020, 20(6), 1599; https://doi.org/10.3390/s20061599 - 13 Mar 2020
Cited by 33 | Viewed by 8809
Abstract
A growing number of devices, from car key fobs to mobile phones to WiFi-routers, are equipped with ultra-wideband radios. In the network formed by these devices, communicating modules often estimate the channel impulse response to employ a matched filter to decode transmitted data [...] Read more.
A growing number of devices, from car key fobs to mobile phones to WiFi-routers, are equipped with ultra-wideband radios. In the network formed by these devices, communicating modules often estimate the channel impulse response to employ a matched filter to decode transmitted data or to accurately time stamp incoming messages when estimating the time-of-flight for localization. This paper investigates how such measurements of the channel impulse response can be utilized to augment existing ultra-wideband communication and localization networks to a multi-static radar network. The approach is experimentally evaluated using off-the-shelf hardware and simple, distributed filtering, and shows that a tag-free human walking in the space equipped with ultra-wideband modules can be tracked in real time. This opens the door for various location-based smart home applications, ranging from smart audio and light systems to elderly monitoring and security systems. Full article
(This article belongs to the Special Issue Radio Sensing and Sensor Networks)
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18 pages, 996 KiB  
Article
Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments
by Ahmed Abdelgawwad, Alireza Borhani and Matthias Pätzold
Sensors 2020, 20(4), 1049; https://doi.org/10.3390/s20041049 - 14 Feb 2020
Cited by 10 | Viewed by 5336
Abstract
This paper is about designing a 3D no n-stationary wideband indoor channel model for radio-frequency sensing. The proposed channel model allows for simulating the time-variant (TV) characteristics of the received signal of indoor channel in the presence of a moving object. The moving [...] Read more.
This paper is about designing a 3D no n-stationary wideband indoor channel model for radio-frequency sensing. The proposed channel model allows for simulating the time-variant (TV) characteristics of the received signal of indoor channel in the presence of a moving object. The moving object is modelled by a point scatterer which travels along a trajectory. The trajectory is described by the object’s TV speed, TV horizontal angle of motion, and TV vertical angle of motion. An expression of the TV Doppler frequency caused by the moving scatterer is derived. Furthermore, an expression of the TV complex channel transfer function (CTF) of the received signal is provided, which accounts for the influence of a moving object and fixed objects, such as walls, ceiling, and furniture. An approximate analytical solution of the spectrogram of the CTF is derived. The proposed channel model is confirmed by measurements obtained from a pendulum experiment. In the pendulum experiment, the trajectory of the pendulum has been measured by using an inertial-measurement unit (IMU) and simultaneously collecting CSI data. For validation, we have compared the spectrogram of the proposed channel model fed with IMU data with the spectrogram characteristics of the measured CSI data. The proposed channel model paves the way towards designing simulation-based activity recognition systems. Full article
(This article belongs to the Special Issue Radio Sensing and Sensor Networks)
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