Sustainable Digital Health Infrastructure: Green Computing Approaches for Large-Scale Telemonitoring Systems

Abstract

The rapid expansion of digital health ecosystems, particularly large-scale telemonitoring systems, has intensified concerns about energy consumption, carbon emissions, and long-term environmental sustainability. As health data volumes grow exponentially, telemonitoring platforms rely on high-performance cloud servers, data centers, IoT sensor networks, and continuous connectivity—creating substantial ecological footprints. This paper explores the integration of green computing techniques to develop sustainable digital health infrastructure capable of supporting scalable telemonitoring operations without compromising clinical performance. The study analyzes low-power sensor architectures, edge computing models, energy-efficient data compression, renewable-powered data centers, and carbon-aware computational scheduling. Through a detailed case study, the research evaluates how sustainable technologies can reduce operational energy consumption and promote environmental responsibility while maintaining system reliability, security, and clinical effectiveness. The findings offer a comprehensive framework for designing next-generation eco-friendly telemonitoring systems and propose policy-level strategies to enhance sustainability in global digital health ecosystems