We propose an IoT network protocol for rural climate systems. It adjusts communication schedules based on solar prediction and mesh reconfiguration, improving network lifespan and reducing energy use in disconnected and power-scarce regions.

Michael Foster, Emily Roberts, Nathan Brooks, Olivia Scott, Sarah Hamilton

This study presents an eco-friendly water monitoring system. It uses solar-powered drones with on-device AI to detect pollutants in rivers, eliminating energy dependencies while supporting sustainable aquatic ecosystem protection.

Chen Min Hao, Liu Xiu Fang, Xu Bo Liang, Zhang Lin Rui, Gao Yu Cheng

This study presents a solar-driven federated learning framework. It adaptively schedules edge device participation based on energy availability, enhancing sustainability and scalability of machine learning in off-grid, eco-sensitive sensor environments.

Amit Suresh Nair, Jean Claude Laurent, Priya Harini Deshmukh, Fatima Noor Al-Saadi, Carlos Enrique Montoya

This paper introduces a cloud workload scheduling mechanism that selects server regions based on real-time emission data. It reduces carbon output while maintaining SLA performance across geographically distributed, energy-aware data centers.

Olivia Simmons, Laura Whitmore, Thomas Greene, Rachel Jenkins, Michael Scott

This research proposes a sustainable monitoring platform for forests. It uses edge AI models on energy-harvesting devices to detect fire risk and wildlife activity without batteries, enabling long-term environmental surveillance.

Chen Ming Jie, Liu Zhi Sheng, Xu Qiang Bo, Zhang Lin Hao, Gao Wen Ze

We present a lifecycle forecasting engine that enables circular hardware reuse in consumer electronics. By predicting failure and evaluating reusable components, it reduces e-waste and supports sustainable smart device manufacturing and operation.

Neha Rajesh Iyer, Jean Pierre Moreau, Taro Kenji Nakamoto, Priya Shalini Nair, Fatima Noor Al-Farouq

This study proposes a task offloading framework for edge devices. It learns thermal patterns and reassigns tasks before overheating occurs, ensuring device safety, reducing energy spikes, and supporting sustainable mobile computing.

Chen Hao Ming, Liu Bo Liang, Xu Zhen Fang, Zhang Rui Jian, Gao Tian Sheng

This paper introduces a sustainable traffic control system using green edge devices. It predicts congestion using compact AI models, reducing idle emissions and improving real-time routing efficiency in smart city infrastructures.

Emily Bennett, Nathan Brooks, Sarah Monroe, James Carter, Laura Simmons