ABSTRACT
Use of electronic devices has increased many times in our daily life. It is used for many purposes including healthcare. Small sensor devices on patients’ body that reads patients’ physiological data and send those data to a remote server. Doctors and other healthcare professionals can view this data sitting at their home. Thus remote health monitoring is possible known as Wireless Body Area Networks (WBAN). Routing and providing seamless connectivity is a big challenge and a topic of research. In this work, a priority based routing protocol designed for WBAN has been developed where data has been classified into normal and emergency data. This routing protocol is especially applicable for COVID and diabetic patients. Normal data will be processed in cloud server but emergency data will be processed locally. Results obtained prove that our protocol is faster and also gives minimum delay. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In December 2019, a novel coronavirus was found in Wuhan, China. The disease spread due to it was named COVID-19 (Coronavirus Disease 2019). Since then, more than 28 lakh people have been affected worldwide by the virus at the time of writing this paper. The virus has claimed the lives of close to 2 lakh people. It has also affected the lives and livelihoods of millions of people around the globe. Thus it is important to predict and analyse how the disease will affect us in the future. This work uses various machine learning and deep learning models to predict the number of COVID-19 cases in the near future and compare the accuracies of the different results thus obtained. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.