SUSTAINABLE EDGE COMPUTING - ENABLING GREEN AND INTELLIGENT FUTURE TECHNOLOGIES

The rapid proliferation of Internet of Things (IoT) devices, artificial intelligence (AI), and data intensive applications has significantly increased the energy demands of conventional cloud computing systems, resulting in higher carbon emissions and operational inefficiencies. Sustainable computing aims to minimize energy consumption and environmental impact; however, traditional cloud infrastructures rely heavily on energy-intensive data centres powered by non-renewable sources.To address these challenges, this paper proposes a sustainable edge computing framework that enables localized data processing, thereby reducing latency, bandwidth usage, and energy consumption. The framework integrates renewable energy-powered edge nodes and AI-driven task scheduling to optimize system performance and resource utilization. Mathematical models for energy consumption, latency, and transmission are developed and validated through numerical analysis, demonstrating energy savings of up to 60 - 99% compared to cloud-based systems. Secure data storage and retrieval mechanisms are also incorporated to ensure data reliability and privacyThe proposed approach is evaluated across real-world applications, including smart cities, healthcare,agriculture, and industrial systems.The results confirm that sustainable edge computing significantly enhances energy efficiency and reduces carbon emissions, making it a key enabler for environmentally responsible and intelligent future technologies.