Scalable AI Solution for IoT Based Healthcare System Using Cloud Platform

Abstract

The Scalable AI solution for IOT based healthcare system using cloud platform is the rapid evolution of Internet of Things (IoT) technologies has significantly transformed modern healthcare by enabling continuous monitoring, real-time data acquisition, and intelligent decision-making for patient care. However, the massive volume, velocity, and variety of health-related data generated by wearable sensors, smart medical devices, and remote monitoring systems present major challenges in terms of data management, processing, scalability, reliability, and security. To address these issues, this work proposes a scalable artificial intelligence (AI) solution integrated with a cloud computing platform to build an efficient, flexible, and intelligent IoT-based healthcare system. The primary objective of this solution is to enhance clinical decision-making, provide personalized health insights, and support large-scale remote health monitoring frameworks, especially in scenarios like chronic disease management, elderly care, and emergency response. In the proposed architecture, IoT devices serve as the first layer, continuously collecting critical physiological signals such as heart rate, blood pressure, glucose level, body temperature, respiratory rate, and electrocardiogram (ECG) data. These devices are lightweight, energy-efficient, and interconnected through wireless communication protocols such as Bluetooth Low Energy (BLE), Wi-Fi, or LPWAN. The data collected at this layer is pre-processed locally using edge AI techniques to filter noise, compress redundant signals, and perform preliminary anomaly detection to reduce the load on the cloud infrastructure.