In this paper, we are presenting an epidemiological model for exploring the transmission of outbreaks caused by viral infections. Mathematics and statistics are still at the cutting edge of technology where scientific experts, health facilities, and government deal with infection and disease transmission issues. The model has implicitly applied to COVID-19, a transmittable disease by the SARS-CoV-2 virus. The SIR model (Susceptible-Infection-Recovered) used as a context for examining the nature of the pandemic. Though, some of the mathematical model assumptions have been improved evaluation of the contamination-free from excessive predictions. The objective of this study is to provide a simple but effective explanatory model for the prediction of the future development of infection and for checking the effectiveness of containment and lock-down. We proposed a SIR model with a flattening curve and herd immunity based on a susceptible population that grows over time and difference in mortality and birth rates. It illustrates how a disease behaves over time, taking variables such as the number of sensitive individuals in the community and the number of those who are immune. It accurately model the disease and their lessons on the importance of immunization and herd immunity. The outcomes obtained from the simulation of the COVID-19 outbreak in India make it possible to formulate projections and forecasts for the future epidemic progress circumstance in India.

For decision-making and governance, smart cities depend on tracking data collected via a substantial percentage of wireless sensing nodes. However, several limitations affect Wireless Sensor Network (WSN)-based Internet of Things (IoT) services, such as low battery life, recurrent connectivity problems due to multi-hop connections, and a limited channel capacity. Furthermore, in many systems, clustering and routing are handled independently, which prevents the adaptation of effective strategies for optimal energy usage and prolonged network lifespan. This research gathers data from heterogeneous IoT nodes linked via WSN and distributed across a smart infrastructure. There are two interrelated problems to be addressed with respect to energy efficiency computations: clustering and routing. We provide a new clustering strategy through which efficient routing of critical and regular data is handled. As a result, both clustering and routing have been significantly strengthened, which balances the communication load across different sectors of the smart infrastructure network. Minkowski distance and ranking strategy are used for routing and selecting cluster heads, respectively. Deterministic distributed–time division multiple access (DD-TDMA) scheduling is employed to balance the communication load across the network. The experimental results show that the proposed work outperforms some of the popular cluster-based routing strategies.