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Sensing, Imaging and Computing in Multimedia and Network
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Sensing, Imaging and Computing in Multimedia and Network

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

Deadline for manuscript submissions: closed (15 August 2021) | Viewed by 20005

Special Issue Editors

Dr. Moayad Aloqaily

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Guest Editor
Moayad Aloqaily, xAnalytics Inc., Ottawa, Canada
Interests: Blockchain; Cybersecurity; AI; Vehicular Networks
Dr. Mohammad Alsmirat

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Guest Editor
Jordan University of Science and Technology, Jordan
Interests: Multimedia Systems; Data Science; Digital Twin; Virtual reality
Dr. Jaime Lloret

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Guest Editor
Integrated Management Coastal Research Institute, Universitat Politecnica de Valencia, 46022 Valencia, Spain
Interests: network protocols; network algorithms; wireless sensor networks; ad hoc networks; multimedia streaming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dealing with a huge amount of data in general, and huge data from different modalities, is very common in today’s systems. With the continuous development in network technologies, acquisition systems, and computational power, creating and sharing data is becoming easier for all people around the world. On Facebook, for example, around 500,000 comments are posted and around 135,000 images are uploaded every second. Storing, searching, analyzing, and utilizing these data are highly challenging problems. That is why the International Conference on Multimedia Computing, Networking and Applications (MCNA) and The International Conference on Intelligent Data Science Technologies and Applications (IDSTA) have come together to give the opportunity for researchers and practitioners to present their efforts in addressing the challenges of dealing with data. Furthermore, we expect that the MCNA and IDSTA and their publications will trigger future related intelligent methods and technologies that will improve the data science field in general. The authors of papers accepted at the MCNA and IDSTA that are related to the topics of the Sensors journal will be invited to submit extended versions of their papers to this Special Issue. Moreover, new papers strictly related to the conferences themes will also be welcomed.

Topics include, but are not limited to, the following:

Energy efficience for IoT

AI-assisted sensing

Intelligent sensing systems

Intelligent multimedia data sensing systems

Sensors in video surveillance systems

Media sensing summarization

Media sensing forensics

Pattern recognition

Neural networks

Deep learning

Process mining

Granular computing

Mobile sensing

Smart city data sensing and management

Management of sensor data

Augmented reality sensing and processing

Sensors for games

Intelligent image/video analytics

Digital image and video processing

Image rendering and quality

Imaging sensors and acquisition systems

Virtual reality and simulations

Augmented reality image processing

Dr. Moayad Aloqaily
Dr. Mohammad Alsmirat
Prof. Dr. Jaime Lloret Mauri
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.

Published Papers (4 papers)

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Research

24 pages, 6593 KiB  
Article
Early Warning Systems for COVID-19 Infections Based on Low-Cost Indoor Air-Quality Sensors and LPWANs
by Nikolaos Peladarinos, Vasileios Cheimaras, Dimitrios Piromalis, Konstantinos G. Arvanitis, Panagiotis Papageorgas, Nikolaos Monios, Ioannis Dogas, Milos Stojmenovic and Georgios Tsaramirsis
Sensors 2021, 21(18), 6183; https://doi.org/10.3390/s21186183 - 15 Sep 2021
Cited by 18 | Viewed by 4649
Abstract
During the last two years, the COVID-19 pandemic continues to wreak havoc in many areas of the world, as the infection spreads through person-to-person contact. Transmission and prognosis, once infected, are potentially influenced by many factors, including indoor air pollution. Particulate Matter (PM) [...] Read more.
During the last two years, the COVID-19 pandemic continues to wreak havoc in many areas of the world, as the infection spreads through person-to-person contact. Transmission and prognosis, once infected, are potentially influenced by many factors, including indoor air pollution. Particulate Matter (PM) is a complex mixture of solid and/or liquid particles suspended in the air that can vary in size, shape, and composition and recent scientific work correlate this index with a considerable risk of COVID-19 infections. Early Warning Systems (EWS) and the Internet of Things (IoT) have given rise to the development of Low Power Wide Area Networks (LPWAN) based on sensors, which measure PM levels and monitor In-door Air pollution Quality (IAQ) in real-time. This article proposes an open-source platform architecture and presents the development of a Long Range (LoRa) based sensor network for IAQ and PM measurement. A few air quality sensors were tested, a network platform was implemented after simulating setup topologies, emphasizing feasible low-cost open platform architecture. Full article
(This article belongs to the Special Issue Sensing, Imaging and Computing in Multimedia and Network)
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21 pages, 9260 KiB  
Article
Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures
by Ananda Ananda, Kwun Ho Ngan, Cefa Karabağ, Aram Ter-Sarkisov, Eduardo Alonso and Constantino Carlos Reyes-Aldasoro
Sensors 2021, 21(16), 5381; https://doi.org/10.3390/s21165381 - 09 Aug 2021
Cited by 16 | Viewed by 4256
Abstract
This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). The CNNs were used to classify a series of wrist radiographs from the Stanford [...] Read more.
This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). The CNNs were used to classify a series of wrist radiographs from the Stanford Musculoskeletal Radiographs (MURA) dataset into two classes—normal and abnormal. The architectures were compared for different hyper-parameters against accuracy and Cohen’s kappa coefficient. The best two results were then explored with data augmentation. Without the use of augmentation, the best results were provided by Inception-ResNet-v2 (Mean accuracy = 0.723, Mean kappa = 0.506). These were significantly improved with augmentation to Inception-ResNet-v2 (Mean accuracy = 0.857, Mean kappa = 0.703). Finally, Class Activation Mapping was applied to interpret activation of the network against the location of an anomaly in the radiographs. Full article
(This article belongs to the Special Issue Sensing, Imaging and Computing in Multimedia and Network)
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28 pages, 1516 KiB  
Article
Smarter Open Government Data for Society 5.0: Are Your Open Data Smart Enough?
by Anastasija Nikiforova
Sensors 2021, 21(15), 5204; https://doi.org/10.3390/s21155204 - 31 Jul 2021
Cited by 41 | Viewed by 6253
Abstract
Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are [...] Read more.
Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are reused. Moreover, the open (government) data initiative as well as users’ intent for open (government) data are changing continuously and today, in line with IoT and smart city trends, real-time data and sensor-generated data have higher interest for users. These “smarter” open (government) data are also considered to be one of the crucial drivers for the sustainable economy, and might have an impact on information and communication technology (ICT) innovation and become a creativity bridge in developing a new ecosystem in Industry 4.0 and Society 5.0. The paper inspects OGD portals of 60 countries in order to understand the correspondence of their content to the Society 5.0 expectations. The paper provides a report on how much countries provide these data, focusing on some open (government) data success facilitating factors for both the portal in general and data sets of interest in particular. The presence of “smarter” data, their level of accessibility, availability, currency and timeliness, as well as support for users, are analyzed. The list of most competitive countries by data category are provided. This makes it possible to understand which OGD portals react to users’ needs, Industry 4.0 and Society 5.0 request the opening and updating of data for their further potential reuse, which is essential in the digital data-driven world. Full article
(This article belongs to the Special Issue Sensing, Imaging and Computing in Multimedia and Network)
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21 pages, 26627 KiB  
Article
Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation
by Alexander A. Kharlamov, Aleksei N. Raskhodchikov and Maria Pilgun
Sensors 2021, 21(12), 3965; https://doi.org/10.3390/s21123965 - 08 Jun 2021
Cited by 16 | Viewed by 3253
Abstract
The article presents the results of the analysis of the adaptation of metropolis IT technologies to solve operational problems in extreme conditions during the COVID-19 pandemic. The material for the study was Russian-language data from social networks, microblogging, blogs, instant messengers, forums, reviews, [...] Read more.
The article presents the results of the analysis of the adaptation of metropolis IT technologies to solve operational problems in extreme conditions during the COVID-19 pandemic. The material for the study was Russian-language data from social networks, microblogging, blogs, instant messengers, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the first wave of the COVID-19 pandemic in Russia. The data were collected between 1 March 2020 and 1 June 2020. The database size includes 85,493,717 characters. To analyze the content of social media, a multimodal approach was used involving neural network technologies, text analysis, sentiment-analysis and analysis of lexical associations. The transformation of old digital services and applications, as well as the emergence of new ones were analyzed in terms of the perception of digital communications by actors. Full article
(This article belongs to the Special Issue Sensing, Imaging and Computing in Multimedia and Network)
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