We propose an IoT routing protocol designed for sustainability. It forecasts harvestable energy and adjusts node duty cycles accordingly, ensuring prolonged network operation in agriculture, climate monitoring, and environmental surveillance deployments.

Chen Rui Liang, Liu Cheng Zhi, Xu Hao Min, Zhang Wen Bo, Gao Lin Yu

This work introduces an AI-powered microgrid control system. It forecasts solar and wind input to balance energy loads across rural grids, increasing power reliability while reducing reliance on non-renewable backup sources.

Neha Malini Ramesh, Jean Claude Boucher, Priya Sushma Kulkarni, Fatima Noor El-Zahra, Carlos Javier Morales

This research presents a sustainable water monitoring system. Powered by environmental energy, it runs compact CNN models on edge devices to detect pollution events, enabling autonomous operation with minimal ecological impact.

Chen Zhi Hao, Liu Feng Qiang, Zhang Ming Hui, Xu Tian Bo, Gao Rong Xi

This paper proposes an emission-aware scaling algorithm for cloud workloads. It dynamically adjusts resource allocation based on real-time carbon intensity, ensuring sustainable operation of applications under variable usage patterns.

Robert Simmons, Emily Watson, Laura Jensen, Sarah Hudson, Benjamin Clarke

This paper introduces a federated edge learning system that adapts client selection based on renewable energy availability. It improves sustainability and training convergence while reducing carbon impact in large-scale edge deployments.

Fatima Noor Al-Hameed, Jean Claude Laurent, Priya Sangeetha Deshmukh, Carlos Eduardo Gutierrez

This study proposes a data center load optimization model. It shifts workloads across zones by forecasting carbon intensity, reducing environmental impact while ensuring uninterrupted cloud service under fluctuating energy conditions.

Emily Simmons, James Carter, Sarah Monroe, Robert Brooks, Olivia Matthews

This paper introduces a solar-driven wildlife monitoring system. Using predictive wake scheduling and lightweight edge classifiers, it enables long-term, maintenance-free biodiversity observation with no battery dependency.

Chen Hao Ming, Liu Bo Sheng, Zhang Rui Wen, Xu Zhi Cheng, Gao Wen Jie

This study presents an AI model that forecasts electronic component wear. It improves device longevity and supports circular reuse strategies, reducing e-waste generation across smart consumer electronics ecosystems.

Neha Kiran Desai, Jean Pierre Rousseau, Priya Harika Ramesh, Taro Kenji Nakamoto, Fatima Noor El-Salem