This study proposes a smart grid-aware task scheduler for cloud workloads. It reallocates computational resources based on carbon forecasts, reducing environmental impact while ensuring SLA compliance in geographically distributed data centers.

Emily Watson, Laura Bennett, Nathan Brooks, Thomas Greene, Rachel Simmons

This paper introduces a temperature-sensitive scheduler that prevents overheating in edge devices. By predicting heat patterns and redistributing tasks, it improves system reliability and energy efficiency in mobile and sensor-intensive computing environments.

Liu Tian Zhang, Xu Bo Qiang, Zhang Min Yu, Gao Zhi Tao

This work presents an AI-powered maintenance engine for electronics. It predicts component failure and recommends reuse routes, reducing e-waste by extending device life through sustainable servicing strategies for smart consumer products.

Sandeep Mehta, Jean Louis Rousseau, Priya Harika Iyer, Carlos Eduardo Morales, Fatima Noor El-Amin

We propose a low-power AI framework that fuses multisensor data for remote climate observation. It uses energy-aware scheduling to align computation with available solar power, ensuring autonomous long-term operation in off-grid deployments.

Zhang Qiang Li, Gao Jian Cheng, Xu Fang Bo

This study introduces a model selection system for sustainable AI in the cloud. It allocates inference requests based on carbon intensity of regions, reducing emissions without sacrificing response times or accuracy in global deployments.

Olivia Peterson, James Clarke, Emily Monroe, Robert Hudson, Sarah Whitmore

We introduce a predictive microgrid management model that forecasts rural electricity usage and reallocates solar energy in real-time. It ensures efficient power utilization and improves sustainability in energy-scarce regions.

Kavitha Iyer, Jean Philippe Moreau, Priya Malini Desai, Fatima Noor Al-Rahim, Carlos Javier Montoya

This paper presents a sustainable traffic control system using solar-powered IoT and edge AI. It predicts congestion and adjusts traffic signals dynamically, cutting urban emissions and reducing fuel waste in smart mobility networks.

Xu Min Hao, Liu Bo Xin, Zhang Ming Rui, Gao Li Cheng

This work proposes a solar-powered drone system for autonomous water inspection. It integrates lightweight AI to detect contamination in real-time, ensuring scalable, sustainable environmental protection in lakes, rivers, and reservoirs.

Sarah Greene, Benjamin Clarke, Olivia Mason