Optimizing Remote Patient Monitoring Security’s Features Managing Compacted Real Time IoT PAYLOAD Deploying Light Weighted Encryption and Foveal Quality Optimization Tools

Abstract

The prevalence of chronic diseases necessitates continuous monitoring and frequent medical consultations, posing challenges in terms of resource availability and timely appointments. Remote Patient Monitoring (RPM) systems offer a solution by enabling continuous monitoring and real-time communication between patients and healthcare providers. The increasing size of transmitted data, energy consumption and the need for real-time responsiveness are identified as key issues. This paper focuses on optimizing the RPM system, with an emphasis on data security and transmission efficiency. Our research aims to enhance the efficiency and reliability of RPM by proposing solutions that optimize communication protocols, data management, and security measures. These findings contribute to the advancement of RPM systems, ensuring effective remote monitoring and timely healthcare interventions for patients with chronic illnesses. The transmission of data from sensors and the communication between nodes and the cloud are examined, considering the security requirements of the IoT platform. To tackle these challenges, the paper presents four optimization approaches/tools for both the system and payload. These tools encompass enhancing communication between the cloud and nodes, optimizing node-level information, reducing payload size prior to data transmission, and implementing data compression techniques in conjunction with a feature-based psychovisual coding approach. The proposed optimizations aim to enhance the overall performance and efficiency of the system, ensuring timely response and reliable data transmission.