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Special Issue "IoT and Artificial Intelligence Approaches to Defeat COVID-19 Outbreak"

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

Deadline for manuscript submissions: closed (15 June 2021).

Special Issue Editors

Dr. A.S.M. Kayes
E-Mail Website
Guest Editor
Department of Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3086, Australia
Interests: different aspects of security, privacy and trust practices to address emergency events such as the COVID-19 outbreak and other e-health measures; data governance and big data applications; Internet of Things and data quality; context-aware access control; data sharing and privacy; security and AI; ransomware detection and defense; IoT security; cloud/fog security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paul Watters
E-Mail Website
Guest Editor
PPDP & Associate Professor, Macquarie University, Sydney, Australia
Interests: examining the links between film piracy and the proliferation of child abuse material online; AI and penetration testing; cybercrime and cyber terrorism; online threats and social harms; malware and ransomware; API security; identity thefts; scams and phishing
Special Issues, Collections and Topics in MDPI journals
Dr. Ebrima Ceesay
E-Mail Website
Co-Guest Editor
Principal Scientist and Lead for Center Cyber of Excellence at Noblis and Adjunct Professor at Johns Hopkins University, USA
Interests: The intersection of computer security and machine learning, with interests in using machine learning to improve software security and in improving the security and reliability of the machine learning models themselves; insider, intrusion, and misuse detection; adaptive and resilience systems; data science and advance analytics.
Dr. Man Qi
E-Mail Website
Co-Guest Editor
Senior Lecturer in Computing, Canterbury Christ Church University, UK
Interests: Internet of Things; Cyber Security; Intelligent Computing and Applications; and HCI.
Dr. Md. Saiful Islam
E-Mail Website
Co-Guest Editor
School of Information Technology, Griffith University, Southport, Australia
Interests: database usability; advanced data analytics; graph data management
Special Issues, Collections and Topics in MDPI journals
Dr. Abdur Rahman Bin Shahid
E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Concord University, Athens, WV, USA
Interests: Internet of Things (IoT); Cyber Security; Machine Learning; Privacy-Preserving Machine Learning; Blockchain; Mobile Computing; Location-Based Applications; Smart City; Security Privacy, and Trust issues with emergency events like COVID-19 pandemics.

Special Issue Information

Dear Colleagues,

Sensors provide valuable data about physical devices and the associated environment. The unprecedented increase in data volumes related to different sensor applications and networks is powering big data analytics through a range of artificial intelligence (AI) techniques. In the context of COVID-19, big data refers to patient healthcare data such as lists of physicians and patients, medical images, physician notes, case history, chest X-ray reports, information about outbreak areas, and so on. These data are generated from a number of sources, ranging from Internet of Things (IoT) sensors (e.g., smartphone data) to online social platforms (e.g., public reactions). The traditional data analytic tools and mechanisms are not adequate for meeting the requirements during the COVID-19 pandemic. For example, two of the new research directions with COVID-19 involve using AI techniques for medical image processing and sentiment analysis toward social distancing. The translation of these big data into concrete actions (e.g., deriving valuable information from people’s opinions toward social distancing measures) requires processing the inputs acquired from sensors and social networks. Such transformation and processing can benefit from the new insights provided by branches of AI, like the use of machine learning and deep learning to improve the COVID-19 pandemic situation and drive further mitigation of the COVID-19 outbreak. 

Authors of selected high-qualified papers from the International Workshop on Security, Privacy, and Trust for Emergency Events (EmergencyComm 2020) will be invited to submit extended versions of their original papers (50% extensions of the contents of the conference paper) and contributions.

Topics of interest include but are not limited to:

  • COVID-19 crisis management and communication strategies;
  • Security, privacy, and trust practices to address events like the COVID-19 outbreak through data from social and IoT networks;
  • Sentiment analysis toward social distancing against COVID-19;
  • AI to process COVID-19 data from IoT sensor networks;
  • AI techniques for medical image processing for COVID-19;
  • Automated messaging to deliver timely and relevant prevention messages against COVID-19;
  • Identifying and blocking scams and other cybercrime tactics involving COVID-19;
  • Measuring community acceptance of social distancing against COVID-19; 
  • The role of messaging and chatbots in engaging concerned users;
  • Privacy-preserving data mining and machine learning for emergency events through IoT ;
  • Modelling and protection of the disease spread and other hazardous consequences;
  • Understanding risks associated with coronavirus infections through AI-based sensor applications; and
  • Identifying social distancing parameters through deep learning architectures along with data from IoT sensor networks
Dr. A.S.M. Kayes
Prof. Dr. Paul Watters
Dr. Ebrima Ceesay
Dr. Man Qi
Dr. Md. Saiful Islam
Dr. Abdur Rahman Bin Shahid

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 papers will be 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 2200 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.

 
 

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Published Papers (3 papers)

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Research

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Article
Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil
Sensors 2021, 21(2), 540; https://doi.org/10.3390/s21020540 - 13 Jan 2021
Cited by 8 | Viewed by 2158
Abstract
São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the [...] Read more.
São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given. Full article
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Review

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Review
Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT
Sensors 2021, 21(11), 3838; https://doi.org/10.3390/s21113838 - 01 Jun 2021
Cited by 1 | Viewed by 3928
Abstract
COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact [...] Read more.
COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption. Full article
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Technical Note
CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19
Sensors 2021, 21(2), 484; https://doi.org/10.3390/s21020484 - 12 Jan 2021
Cited by 5 | Viewed by 1199
Abstract
The factors affecting the penetration of certain diseases such as COVID-19 in society are still unknown. Internet of Things (IoT) technologies can play a crucial role during the time of crisis and they can provide a more holistic view of the reasons that [...] Read more.
The factors affecting the penetration of certain diseases such as COVID-19 in society are still unknown. Internet of Things (IoT) technologies can play a crucial role during the time of crisis and they can provide a more holistic view of the reasons that govern the outbreak of a contagious disease. The understanding of COVID-19 will be enriched by the analysis of data related to the phenomena, and this data can be collected using IoT sensors. In this paper, we show an integrated solution based on IoT technologies that can serve as opportunistic health data acquisition agents for combating the pandemic of COVID-19, named CIoTVID. The platform is composed of four layers—data acquisition, data aggregation, machine intelligence and services, within the solution. To demonstrate its validity, the solution has been tested with a use case based on creating a classifier of medical conditions using real data of voice, performing successfully. The layer of data aggregation is particularly relevant in this kind of solution as the data coming from medical devices has a very different nature to that coming from electronic sensors. Due to the adaptability of the platform to heterogeneous data and volumes of data; individuals, policymakers, and clinics could benefit from it to fight the propagation of the pandemic. Full article
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