Resource allocation in edge computing is a highly active research domain, focusing on optimizing the distribution of computational, storage, and networking resources across heterogeneous and geographically distributed edge environments. Research papers in this field address the challenge of meeting diverse application requirements such as low latency, high reliability, energy efficiency, and scalability for use cases including smart healthcare, industrial IoT, connected autonomous vehicles, and augmented reality. Studies investigate task scheduling, load balancing, and joint optimization of communication-computation trade-offs, often under constraints of user mobility, limited edge resources, and dynamic workloads. Recent works employ optimization techniques, game theory, machine learning, and deep reinforcement learning to design adaptive and intelligent resource allocation strategies. Security- and privacy-aware resource allocation is also a growing focus, ensuring trustworthy collaboration among multiple tenants and preventing malicious exploitation. Furthermore, hybrid approaches that integrate cloud, fog, and edge resources are being studied to enhance flexibility and service continuity. Overall, research in this area demonstrates that efficient and intelligent resource allocation is crucial for realizing the full potential of edge computing in next-generation cyber-physical systems.