Research on Resource Scheduling in Edge Computing focuses on efficiently managing and allocating computational, storage, and network resources at the edge of the network to meet latency-sensitive and bandwidth-constrained application requirements. This area aims to optimize performance, energy consumption, cost, and Quality of Service (QoS) while addressing the challenges of resource heterogeneity, mobility, and dynamic workloads. Key research directions include task offloading and scheduling algorithms, workload-aware and context-aware resource allocation, and heuristic or metaheuristic approaches (e.g., genetic algorithms, particle swarm optimization) for efficient scheduling. Other emerging topics involve energy-efficient and green edge resource management, multi-objective optimization balancing latency, cost, and resource utilization, edge–cloud integrated scheduling frameworks, and predictive or machine learning-driven scheduling for dynamic environments. Additionally, research on fault-tolerant, adaptive, and SLA-compliant resource scheduling, as well as orchestration across heterogeneous edge nodes and IoT devices, represents significant avenues for advancing intelligent and efficient edge computing systems.