The field of edge computing and IoT systems is moving towards a more sustainable and efficient direction. Researchers are focusing on developing innovative solutions that prioritize energy efficiency, reduce carbon footprint, and improve overall system reliability. Notable advancements include the development of novel edge-based architectures, such as Device-First Continuum AI, which enables autonomous operations in energy sector applications. Additionally, there is a growing emphasis on integrating artificial intelligence and machine learning techniques to optimize edge computing performance, such as predictive data reduction and batch denoising for AIGC service provisioning. Moreover, the importance of energy management systems in smart ports is being recognized, with studies demonstrating significant reductions in energy consumption and carbon emissions. Furthermore, researchers are exploring new approaches to anomaly detection, such as the use of autoencoder-isolation forest hybrids, which prioritize both accuracy and environmental sustainability. Overall, the field is witnessing a significant shift towards more sustainable, efficient, and reliable edge computing and IoT systems. Noteworthy papers include: Joint Edge Server Deployment and Computation Offloading, which introduces a multi-timescale stochastic programming framework for optimizing edge server deployment and computation offloading. EcoDefender, a sustainable hybrid anomaly detection framework that integrates autoencoder-based representation learning with isolation forest anomaly scoring, achieving up to 94% detection accuracy with reduced energy consumption.