The field of edge computing and wireless networks is rapidly evolving, with a focus on improving latency, efficiency, and reliability. Recent developments have centered around the integration of edge computing with various wireless technologies, such as HAPS, UAVs, and non-terrestrial networks, to enable low-latency and high-efficiency data processing. Researchers are also exploring the use of artificial intelligence, machine learning, and deep reinforcement learning to optimize task offloading, resource allocation, and network management. Notable advancements include the development of novel algorithms and frameworks for task scheduling, transmission diversity, and jamming-aware scheduling, which have shown significant improvements in performance and efficiency. Furthermore, the application of edge computing in areas such as public safety, disaster response, and smart manufacturing is being investigated, highlighting the potential of these technologies to drive innovation and improve real-world outcomes. Noteworthy papers include 'Age of Information Optimization in Laser-charged UAV-assisted IoT Networks' which proposes a multi-agent deep reinforcement learning method to minimize peak AoI, and 'EC-Diff: Fast and High-Quality Edge-Cloud Collaborative Inference for Diffusion Models' which accelerates cloud inference through gradient-based noise estimation and identifies the optimal point for cloud-edge handoff.