INTEGRATING IOT DEVICES WITH CLOUD HEALTHCARE SYSTEMS FOR ENHANCED DATA MANAGEMENT

Abstract

The rapid advancements in Internet of Things-based healthcare systems have revolutionized patient monitoring by enabling continuous and data collection from various devices like heart rate monitors, blood pressure sensors, and glucose meters. However, existing systems face critical challenges such as scalability issues and inconsistent data quality, which hinder their effectiveness in applications. This paper proposes a novel framework that addresses these challenges by designing a robust framework that seamlessly integrates IoT devices with cloud healthcare systems, focusing on enhancing data management. The framework starts with data collection from IoT devices, followed by data preprocessing using k-Nearest Neighbors for handling missing values and Z-score normalization for consistent sensor data. The data is then encrypted using the ChaCha20 encryption algorithm. The encrypted data is stored in cloud storage, which facilitates scalability and efficient management of large datasets. The system’s performance is evaluated based on metrics such as encryption time and latency time, with results showing encryption times increasing from 0.09 seconds to 0.25 seconds as the data size grows, and latency times ranging from 0.05 seconds to 0.35 seconds with larger data volumes. The proposed framework contributes to efficient and scalable healthcare data management, ensuring reliable and timely patient monitoring.