In response to the coronavirus (COVID-19) pandemic, Government and public health authorities around the world are developing contact tracing apps as a way to trace and slow the unfold of the virus. There is major divergence among nations, however, between a "privacy-first" approach that protects citizens' information at the price of very restricted access for public health authorities and a "data-first" approach that stores massive amounts of knowledge that, whereas of immeasurable price to epidemiologists. Contact tracing apps work by gathering information from people who have tested positive for the virus and so locating and notifying individuals with whom those people are in shut contact, oftentimes by use of GPS, Bluetooth, or wireless technology. All of the user's information is employed and picked up, the study found that users' information would be created anonymous, encrypted, secured, and can be transmitted on-line and stored solely in an aggregated format. Contact tracing apps use either a centralized or a decentralized approach to work the user's information. Apps that use a centralized approach have high privacy risks. In this paper, the researcher's contributions related to the security and privacy of Contact tracing apps have been discussed and, later research gaps have been identified with proposed solutions.
In recent years, the rise in the demand for quality products and services along with systems that could integrate the control mechanisms with high computational capabilities led to the evolution of cyber-physical systems (CPS). Due to the ongoing COVID-19 pandemic, several industries have remained closed, causing several monetary losses. Automation can help in such scenarios to keep the industries up and running in a way that the system could be monitored and controlled remotely using voice. The chapter deals with the integration of both industrial automation and cyber-physical systems in various industries like the automobile industry, manufacturing industries, construction industries, and so on. A proposed approach for machine handling using CPS, deep learning, and industrial automation with the help of voice. The proposed approach provides greater insights into the application of CPS in the area and the combination of CPS and deep learning to a greater extent.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.