Paper Title
Serverless Architecture for Real-Time loT Data Processing in the Cloud

Abstract
The increased use of IoT in healthcare makes it evident that scalable and responsive systems with the ability to process real-time physiological data are required. In this project, the author will introduce a serverless cloud architecture that will be used to track key health parameters like blood pressure, oxygen saturation, and heart rate of ICU patients. The system uses sensor data streams to process real time data with low latency and scalability by taking advantage of cloudnative applications such as AWS Lambda. It incorporates the use of MQTT to transmit data effectively and stores the metrics of health safely in cloud databases to analyze them further. Besides constant monitoring, the system provides an alert to the medical personnel immediately the different vital signs were out of pre-established thresholds, thereby responding promptly to emergencies. It has also been supported by longitudinal data analytics to determine the effectiveness of drugs, and this contributes to the personalization and informed choice of treatment. The architecture has shown a dependable and inexpensive manner of monitoring healthcare in the present-day with serverless computing.