ABSTRACT
The wide distribution of access points in Izmir allows the collected information to be employed in smart city algorithms. In this study, we analyze the information that has been made publicly available by Izmir Metropolitan Municipality. We first show that the data is reliable, then analyze it from the perspectives of holidays, seasonal trends, and the COVID-19 pandemic. The study also shows that the information can be used for crowd analysis and forecasting, using K-means and SARIMA algorithms, respectively. © 2022 IEEE.
ABSTRACT
Covid-19 is one of the life-threatening diseases which requires intensifying attention to combat disease by designing a smart and effective healthcare system for patients towards diagnosing and managing the Covid-19 disease. Various systems have been developed for diagnosing patient with diseases, but intelligent and feasible solution to explore and monitor the accurate predictive health conditions of affected patients has not been provided yet. In this paper, a new Contactless IoT-enabled cloud-assisted health monitoring system has been designed and developed. The system is made up of unobtrusive sensors, a data acquisition unit, a microcontroller, wi-fi Module, Web server, and Web application or mobile application. It illustrates the design of the system to monitor and detect the severity of the coronavirus in the patients using various unobtrusive sensors to measure disease-specific vital parameters such as heart rate, temperature, oxygen level and pulse rate as main symptoms of the coronavirus are high fever, fatigue, and difficult breathing. Sensor acquired patient data is transformed using the HTTP protocol to cloud server using microcontroller and wi-fi module in real-time. Transformed data of patient condition is processed in the cloud server using data predictive algorithms such as Severity Defined Convolution Neural Network with respect to data collected and severity specific data thresholds and severity class predicted patient information will alarm the healthcare provider on the abnormalities detected in the patient health. A particular model is capable of forecasting the health situation of the patients. Experimental analysis of the proposed architectures finds it effective in monitoring the status of the severity of breathing on the patients. Finally, the performance of the architecture is validated over accuracy and scalability measures. © 2022 IEEE.
ABSTRACT
This work includes an experience of implementation and testing of a virtual classroom developed from the use of a Wi- Fi antenna. It was conceived to be used in indigenous communities where there is no internet or any other telecommunications service, and arises as a necessity for the continuity of educational services during social displacement restrictions decreed by the Peruvian government since March 2020. The set of biosecurity measures decreed by the Peruvian government includes the continuity of educational services through distance mechanisms, which includes the use of traditional means of communication or digital media. Most of the indigenous peoples of the Amazon have neither traditional nor digital media coverage, which also generates costs that are largely unaffordable for families. This has generated the stagnation and intermittency of educational services in most indigenous peoples of the Peruvian Amazon. We propose the use of a medium range Wi-FI antenna to be used in indigenous communities to connect as many students as possible from their homes without physical interaction. Functional tests have been carried out on the types of services (electronic blackboards, chats, videoconferencing applications, file exchange, use of software, etc.), with the participation of 14 students. Among the most relevant results, it is possible to conduct virtual classes with the school population;however, it is still necessary to find or experiment with different configurations regarding antenna coverage (in the future we will experiment with more powerful Wi- Fi antennas), energy consumption and even aspects of the connection and computational performance of the personal computer used as the server. © 2021 IEEE.