The field of edge computing is moving towards optimizing task allocation and resource management in complex networks. Researchers are developing innovative frameworks and algorithms to address the challenges of computational, communication, and energy limitations in edge devices. A key direction is the integration of multiple edge devices, such as unmanned aerial vehicles (UAVs) and low earth orbit (LEO) satellites, to provide efficient computing services. Another important area is the development of energy-efficient resource management strategies in microservices-based fog and edge computing. Noteworthy papers include:

• A paper proposing a binary integer linear programming based formulation for task allocation in the edge/hub/cloud paradigm, which yields optimal and scalable results.
• A paper presenting a diffusion model-enhanced multi-objective optimization framework for improving forest monitoring efficiency in UAV-enabled Internet-of-Things, which reduces motion energy consumption and computing resource by a significant margin.
• A paper proposing a double-edge-assisted computation offloading and resource allocation scheme for space-air-marine integrated networks, which minimizes energy consumption under latency constraints.
• A comprehensive survey of state-of-the-art resource management strategies in microservices-based fog and edge computing, which highlights the lack of synergy among fundamental resource management components and outlines promising research directions.