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) | Viewed by 50722
Special Issue Editors
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
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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
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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.
Interests: Internet of Things; cyber security; intelligent computing and applications; HCI
Special Issues, Collections and Topics in MDPI journals
Interests: big data; data management; AI; predictive analytics; and health informatics
Special Issues, Collections and Topics in MDPI journals
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
Prof. Dr. Paul Watters
Dr. Ebrima Ceesay
Dr. Man Qi
Dr. Md. Saiful Islam
Dr. Abdur Rahman Bin Shahid
Guest Editors
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