VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T14:55:15ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2020-12-25T10:22:48Z2020-12-26T05:21:54Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4333This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/43332020-12-25T10:22:48ZA Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing——Part II: Emerging Technologies and Open IssuesThis two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.Thanh Cong NguyenSaputra Yuris MulyaYurisMulya.Saputra@student.uts.edu.auVan Nguyen HuynhNgoc Tan Nguyennguyen.tan170@gmail.comViet Khoa Trankhoatv.uet@vnu.edu.vnTuan Bui Minhtuanbm.uet@vnu.edu.vnNguyen DiepDiep.Nguyen@uts.edu.auThai Hoang DinhHoang.Dinh@uts.edu.auXuan Thang Vuthang.vu85@gmail.comDutkiewicz Erykeryk.dutkiewicz@uts.edu.auChatzinotas SymeonOttersten Bjorn2020-12-25T10:12:57Z2020-12-25T10:12:57Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4332This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/43322020-12-25T10:12:57ZA Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling TechnologiesSocial distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effectin social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacypreserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.Thanh Cong NguyenSaputra Yuris MulyaYurisMulya.Saputra@student.uts.edu.auNgoc Tan Nguyennguyen.tan170@gmail.comViet Khoa Trankhoatv.uet@vnu.edu.vnMinh Tuan Buituanbm.uet@vnu.edu.vnNguyen DiepThai Hoang DinhXuan Thang Vuthang.vu85@gmail.comDutkiewicz Erykeryk.dutkiewicz@uts.edu.auChatzinotas SymeonOttersten Bjorn2020-07-10T05:39:58Z2020-07-10T05:40:05Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4000This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/40002020-07-10T05:39:58ZCollaborative learning model for cyberattack detection systems in IoT industry 4.0Although the development of IoT Industry 4.0 has brought breakthrough achievements in many sectors, e.g., manufacturing, healthcare, and agriculture, it also raises many security issues to human beings due to a huge of emerging cybersecurity threats recently. In this paper, we propose a novel collaborative learning-based intrusion detection system which can be efficiently implemented in IoT Industry 4.0. In the system under consideration, we develop smart "filters" which can be deployed at the IoT gateways to promptly detect and prevent cyberattacks. In particular, each filter uses the collected data in its network to train its cyberattack detection model based on the deep learning algorithm. After that, the trained model will be shared with other IoT gateways to improve the accuracy in detecting intrusions in the whole system. In this way, not only the detection accuracy is improved, but our proposed system also can significantly reduce the information disclosure as well as network traffic in exchanging data among the IoT gateways. Through thorough simulations on real datasets, we show that the performance obtained by our proposed method can outperform those of the conventional machine learning methods.Khoa Tran Vietkhoatv.uet@vnu.edu.vnSaputra Yuris MulyaYurisMulya.Saputra@student.uts.edu.auHoang Dinh ThaiHoang.Dinh@uts.edu.auTrung Nguyen Linhlinhtrung@vnu.edu.vnDiep Nguyen NDiep.Nguyen@uts.edu.auHa Nguyen Viethanv@vnu.edu.vnDutkiewicz Erykeryk.dutkiewicz@uts.edu.au