This work proposes an AI-based predictive maintenance strategy for electronics. It detects failure patterns and enables part-level reallocation, supporting circular economy principles while minimizing electronic waste in consumer and industrial devices.

Neha Kavitha Subramanian, Jean Pierre Moreau, Taro Kenji Nakamoto, Maria Elena Bianchi, Priya Meenakshi Desai

This paper presents a sustainable AI system for biodiversity monitoring. Powered by solar energy, it runs compact models for environmental event recognition, enabling long-term operation in protected habitats without grid dependency or battery replacement.

Chen Ming Tao, Liu Zhi Hao, Zhang Xiu Fang, Xu Bo Cheng, Gao Wen Liang

This study introduces a routing protocol that considers emission impact in IoT networks. It adapts node roles and data paths dynamically based on energy origin, promoting greener communication in environmental and smart city applications.

Robert Clark, Emily Jensen, Laura Patterson, Thomas Greene, Rachel Bennett

We propose a microgrid model that detects faults using AI and redistributes loads according to real-time renewable availability. This improves energy resilience while reducing fossil reliance in remote and hybrid power systems.

Neha Sandeep Iyer, Jean Louis Rousseau, Fatima Noor Al-Fahad, Priya Sanjana Kulkarni, Carlos Javier Morales

This work introduces a thermal-aware task offloading framework for edge devices. It minimizes overheating by profiling workload demands and dynamically relocating tasks, enhancing energy efficiency and prolonging hardware health in real-time deployments.

Chen Yu Liang, Xu Jian Zhe, Liu Cheng Ming, Zhang Wei Hui, Gao Ping Tao

This research develops a congestion prediction system powered by solar-driven edge processors. It uses deep learning for forecasting traffic, helping reduce emissions from idling vehicles and supporting sustainable urban traffic management.

Michael Anderson, Olivia Collins, Emily Roberts, Nathaniel Scott, Sarah Hudson

We propose a sustainable agriculture IoT system using energy-harvesting nodes. It predicts solar yield to plan task execution and supports on-device AI for crop analysis, ensuring continuous farming support without energy dependency.

Chen Hao Wen, Liu Fang Zhen, Gao Rui Sheng, Xu Min Cheng, Zhang Rong Liang