Optimized serverless cloud-based real-time processing architecture for sensor data analytics in healthcare

In the era of pervasive digital transformation, the need for real-time data processing and intelligent analytics has become paramount, especially in critical sectors like healthcare. This paper presents a novel serverless cloud-based architecture designed for efficient sensor data analytics in real-time healthcare environments. The proposed model integrates modular components including API gateways, serverless functions, stream buffering, and analytical engines to ensure scalability, fault tolerance, and cost-efficiency with higher throughput. Nature-inspired optimization technique is included to expedite data search in the server, for the same a comparative study has been conducted on a hybrid model of Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) it is used to augment data discovery in uninformed search environments. Experimental evaluations using AWS Lambda, S3, and API Gateway reveal the proposed model&s;s superior performance in execution time, accuracy, and scalability under varying load conditions. This architecture demonstrates significant promise for healthcare applications requiring intelligent, responsive, and resource-efficient cloud solutions.