Optimizing Security for Remote Patient Monitoring with Edge Computing Strategies

Abstract

Remote Patient Monitoring (RPM) is a healthcare technology that allows healthcare providers to monitor patients' health remotely using various medical devices and communication technologies. IoT facilitates the integration of diverse medical devices and sensors into a cohesive RPM system. The collected data is transmitted in real-time to healthcare providers or data collection centres where it is analysed and interpreted. During the COVID-19, healthcare facilities faced immense pressure to accommodate a surge in patients. RPM offered a way to monitor and manage non-critical patients remotely, and reduces the risk of exposure to infectious diseases, for both patients and healthcare providers. The traditional approach of data collection was cloud-based platform. In recent times, edge computing has emerged as a promising alternative to traditional cloud-centric architectures, offering solutions to their inherent limitations. In this approach, computation and data storage are closer to the data source, and offers lower latency, reduced bandwidth consumption, and enhanced privacy and security. However, in practical implementation, several factors must be considered, including the scalability, interoperability, and cost-effectiveness of edge computing solutions. Utilizing the close proximity, decentralized processing, and real-time analytics functionalities of edge computing, it is possible to tackle the security issues inherent in RPM systems while maintaining efficient data transmission and processing. This paper proposes a pioneering method for enhancing security in Remote Patient Monitoring (RPM) by integrating edge computing strategies.