Vehicular Network based Emergency Data Transmission and Classification for Health Care System using Support Vector Machine

Abstract

COVID-19 pandemic has created an emergency across the globe. The number of corona positive cases and death cases are still rising across the world. The government of all countries is taking various steps to control the infection of COVID-19. One of the steps to control the spreading of the coronavirus is to quarantine. But the number of active cases at the quarantine center is increasing day by day. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for the monitoring of people at the quarantine center by two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase involves component NIBs, RSUs, and vehicles. For the transmission of data from the quarantine center to the observation center, an effective route is determined using route value. The route-value is dependent on factors like density, shortest path, delay, vehicular data carrying delay, and attenuation. The analysis of health data is done by using multi-class SVM. Finally, simulation results are obtained for the two phases. The simulation results obtained for the health data transmission phase is E2E delay, number of network gaps, and packet delivery ratio. The health data analysis phase determines parameters such as precision, recall, accuracy, etc.