The recent developments in the research area highlight a significant shift towards integrating Internet of Things (IoT) technologies with advanced machine learning and deep reinforcement learning techniques to solve complex environmental and communication challenges. Innovations are particularly focused on enhancing efficiency, reliability, and sustainability across various applications, from wildfire monitoring and aquaculture to satellite communications and energy-efficient networks. A common theme is the use of IoT devices for real-time data collection and monitoring, coupled with machine learning algorithms for data analysis and decision-making. Deep reinforcement learning is increasingly applied to optimize system performance, such as in dynamic resource allocation and energy efficiency improvements. These advancements not only demonstrate the potential for significant operational cost savings but also contribute to environmental conservation and the sustainable management of resources.

Noteworthy papers include:

• A novel IoT architecture for wildfire monitoring that combines machine vision with deep reinforcement learning, enabling automated surveillance over large areas with reduced false positives.
• An IoT-based framework for aquaculture that utilizes machine learning to analyze water quality data, facilitating efficient fish farming practices.
• A federated deep reinforcement learning approach for optimizing energy efficiency in multi-functional RIS-assisted low-Earth orbit networks, showcasing significant improvements over traditional methods.
• A deep reinforcement learning method for enhancing energy efficiency in satellite IoT optical downlinks by leveraging weather forecasts, demonstrating superior performance compared to simpler approaches.
• Research on improving the reliability of LR-FHSS direct-to-satellite IoT through message replication, offering scalable solutions for data transfer reliability without the need for acknowledgments.