VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T10:55:47ZEPrintshttp://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 Bjorn2020-07-10T05:40:31Z2020-07-10T05:41:33Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4001This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/40012020-07-10T05:40:31ZAutoencoder based friendly jammingPhysical layer security (PLS) provides lightweight security solutions in which security is achieved based on the inherent random characteristics of the wireless medium. In this paper, we consider the PLS approach called friendly jamming (FJ), which is more practical thanks to its low computational complexity. State-of-the-art methods require that legitimate users have full channel state information(CSI)of their channel.Thanks to the recent promising application of the autoencoder (AE) in communication, we propose a new FJ method for PLS using AE without prior knowledge of the CSI. The proposed AE-based FJ method can provide good secrecy performance while avoiding explicit CSI estimation. We also apply the recently proposed tool for mutual information neural estimation (MINE) to evaluate the secrecy capacity. Moreover, we leverage MINE to avoid end-to-end learning in AE-based FJ.Tuan Bui Minhtuanbm.uet@vnu.edu.vnTuyen Ta Ductuyentd@vnu.edu.vnTrung Nguyen Linhlinhtrung@vnu.edu.vnHa Nguyen Viethanv@vnu.edu.vn