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Communications Signal Processing and Networking in the Pandemic
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Communications Signal Processing and Networking in the Pandemic

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

Deadline for manuscript submissions: closed (20 March 2021) | Viewed by 14773

Special Issue Editors

Prof. Riccardo Raheli

E-Mail Website
Guest Editor
Università di Parma, Dipartimento di Ingegneria e Architettura, Parco Area delle Scienze 181A, 43124 Parma, Italy
Interests: wireless communication; multimedia signal processing
Prof. Luigi Atzori

E-Mail Website
Guest Editor
University of Cagliari, Italy
Interests: Internet of Things architecture; multimedia communications

Special Issue Information

It is a matter of fact that we are living in an exceptional period of modern history that is demanding us to deeply change our lifestyle—especially with reference to the way we work, conduct social activities, practice sport, go out for dinner, and visit other places, just to cite a few. This is happening due to an enemy, a virus, of which some had foreseen the arrival but only few have worked to prepare our society to fight. We now find ourselves having to decide on the strategy, to design the weapons, and to use them to fight against this unexpected enemy, and all this requires putting together the knowledge, intelligence, and tools that humans have developed so far in all scientific fields.

In this respect, communications signal processing and networking technologies represent some of the most prominent ones, as they can significantly help in one of the most effective strategies to undertake the fight—that is, the 3T plan (test, track, and treat). Indeed, the “track” component can significantly benefit from solutions based on the processing of personal device signals. We also know that the crowding of people should be avoided, and that crowding conditions could be automatically detected (e.g., by the use of optical systems, Lidar techniques, and WiFi sniffing solutions). Furthermore, the communication infrastructure is playing a key role in enabling our daily work and leisure activities, while preserving the physical distance among individuals as required to avoid the spread of the virus. Remarkably, this infrastructure has been able to support an unexpected, unforeseeable, sudden and significant traffic increase.

This Special Issue solicits innovative contributions from both academia and industry in the field of communications signal processing and networking systems, architectures, tools, and devices that may help humanity effectively face this unprecedented pandemic scenario.

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

  • Signal processing for people counting (e.g., WiFi sniffing, video and audio processing, Lidar systems);
  • Prediction of virus spreading;
  • Systems for contact tracing;
  • Modelling of virus spreading in complex networks;
  • Signal processing for distance estimation;
  • Sensors for health condition estimation and diagnosis;
  • Solutions for crowd alerting;
  • System for patient monitoring;
  • Solutions for digital learning during the pandemic;
  • Solutions for smart working during the pandemic;
  • Robust networking solutions in the pandemic;
  • Privacy issues and privacy-preserving techniques during contact tracing.

Prof. Riccardo Raheli
Prof. Luigi Atzori
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

  • People counting 
  • Contact tracing 
  • Virus spreading in complex networks 
  • Signal processing for distance estimation 
  • Sensors for health condition estimation 
  • System for crowd alerting 
  • Digital learning 
  • Smart working

Published Papers (4 papers)

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Research

19 pages, 37214 KiB  
Article
Monitoring Indoor People Presence in Buildings Using Low-Cost Infrared Sensor Array in Doorways
by Cristian Perra, Amit Kumar, Michele Losito, Paolo Pirino, Milad Moradpour and Gianluca Gatto
Sensors 2021, 21(12), 4062; https://doi.org/10.3390/s21124062 - 12 Jun 2021
Cited by 21 | Viewed by 6612
Abstract
We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people’s direction, which allows monitoring users’ occupancy in any space of the building and also [...] Read more.
We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people’s direction, which allows monitoring users’ occupancy in any space of the building and also respects people privacy. The device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor. The computed information is transferred through a Z-Wave network. On-field evaluation of the algorithm has been conducted by placing the device on top of doorways in offices and laboratory rooms. To evaluate the performance of the algorithm in varying ambient temperatures, two groups of stress tests have been designed and performed. These tests established the detection limits linked to the difference between the average ambient temperature and perturbation. For an in-depth analysis of the accuracy of the algorithm, synthetic data have been generated considering temperature ranges typical of a residential environment, different human walking speeds (normal, brisk, running), and distance between the person and the sensor (1.5 m, 5 m, 7.5 m). The algorithm performed with high accuracy for routine human passage detection through a doorway, considering indoor ambient conditions of 21–30 °C. Full article
(This article belongs to the Special Issue Communications Signal Processing and Networking in the Pandemic)
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13 pages, 2095 KiB  
Communication
HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting
by Naveed Ilyas, Boreom Lee and Kiseon Kim
Sensors 2021, 21(10), 3483; https://doi.org/10.3390/s21103483 - 17 May 2021
Cited by 11 | Viewed by 2164
Abstract
Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments. However, crowd counting in high perspective-varying scenes (images) is getting harder due to different density levels [...] Read more.
Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments. However, crowd counting in high perspective-varying scenes (images) is getting harder due to different density levels occupied by the same number of pixels. In this way large variations for objects in the same spatial area make it difficult to count accurately. Further, existing CNN-based crowd counting methods are used to extract rich deep features; however, these features are used locally and disseminated while propagating through intermediate layers. This results in high counting errors, especially in dense and high perspective-variation scenes. Further, class-specific responses along channel dimensions are underestimated. To address these above mentioned issues, we therefore propose a CNN-based dense feature extraction network for accurate crowd counting. Our proposed model comprises three main modules: (1) backbone network, (2) dense feature extraction modules (DFEMs), and (3) channel attention module (CAM). The backbone network is used to obtain general features with strong transfer learning ability. The DFEM is composed of multiple sub-modules called dense stacked convolution modules (DSCMs), densely connected with each other. In this way features extracted from lower and middle-lower layers are propagated to higher layers through dense connections. In addition, combinations of task independent general features obtained by the former modules and task-specific features obtained by later ones are incorporated to obtain high counting accuracy in large perspective-varying scenes. Further, to exploit the class-specific response between background and foreground, CAM is incorporated at the end to obtain high-level features along channel dimensions for better counting accuracy. Moreover, we have evaluated the proposed method on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B), and Venice. The performance of the proposed technique justifies its relative effectiveness in terms of selected performance compared to state-of-the-art techniques. Full article
(This article belongs to the Special Issue Communications Signal Processing and Networking in the Pandemic)
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11 pages, 513 KiB  
Communication
CIR-Based Device-Free People Counting via UWB Signals
by Mauro De Sanctis, Aleandro Conte, Tommaso Rossi, Simone Di Domenico and Ernestina Cianca
Sensors 2021, 21(9), 3296; https://doi.org/10.3390/s21093296 - 10 May 2021
Cited by 7 | Viewed by 2986
Abstract
The outbreak of COVID-19 has resulted in many different policies being adopted across the world to reduce the spread of the virus. These policies include wearing surgical masks, hand hygiene practices, increased social distancing and full country-wide lockdown. Specifically, social distancing involves keeping [...] Read more.
The outbreak of COVID-19 has resulted in many different policies being adopted across the world to reduce the spread of the virus. These policies include wearing surgical masks, hand hygiene practices, increased social distancing and full country-wide lockdown. Specifically, social distancing involves keeping a certain distance from others and avoiding gathering together in large groups. Automatic crowd density estimation is a technological solution that could help in guaranteeing social distancing by reducing the probability that two persons in a public area come in close proximity to each other while moving around. This paper proposes a novel low complexity RF sensing system for automatic people counting based on low cost UWB transceivers. The proposed system is based on an ordinary classifier that exploits features extracted from the channel impulse response of UWB communication signals. Specifically, features are extracted from the sorted list of singular values obtained from the singular value decomposition applied to the matrix of the channel impulse response vector differences. Experimental results achieved in two different environments show that the proposed system is a promising candidate for future automatic crowd density monitoring systems. Full article
(This article belongs to the Special Issue Communications Signal Processing and Networking in the Pandemic)
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26 pages, 6133 KiB  
Article
Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables
by Alex Minetto, Andrea Nardin and Fabio Dovis
Sensors 2021, 21(8), 2588; https://doi.org/10.3390/s21082588 - 07 Apr 2021
Cited by 7 | Viewed by 1904
Abstract
In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring [...] Read more.
In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the user’s locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort. Full article
(This article belongs to the Special Issue Communications Signal Processing and Networking in the Pandemic)
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