A Green IoT Framework for Sustainable Smart Campus Energy Monitoring and Management

The increasing energy demands of modern educational institutions have made sustainable energy management a critical challenge for smart campuses. This paper proposes a comprehensive Green IoT framework designed to monitor, analyse, and optimize energy consumption across campus environments. The proposed system integrates low-power LoRaWAN communication, context-aware adaptive sampling, and edge-level anomaly detection to enable energy-efficient data acquisition and real-time decision-making. Sensor nodes deployed across buildings measure key environmental and electrical parameters, dynamically adjusting their sampling rate based on usage patterns and occupancy. A lightweight statistical anomaly detection algorithm is employed at the edge to detect abnormal consumption patterns and trigger corrective actions. The system's performance was evaluated using metrics such as energy consumption, packet delivery ratio, latency, throughput, node lifetime, and energy savings, and compared against two recent benchmark models from the literature. Simulation results reveal that the proposed solution reduces energy consumption by up to 48%, extends node lifetime by 31%, and improves data delivery reliability. This work contributes a scalable, low-power, and adaptive framework suitable for educational institutions aiming to minimize carbon footprint and promote sustainability through smart technology.