Xiaojun Wu

Papers

Total Citations

H-Index

About

Xiaojun Wu’s research focuses on optimizing industrial robotics workflows within edge-cloud environments, addressing critical challenges in resource allocation, system reliability, and live migration. His major contributions include developing a bi-objective optimization framework for resource allocation in industrial robot monitoring systems (IRMS), balancing performance and cost in dynamic edge-cloud settings. He further advanced the field with a multi-container migration strategy optimization using a hybrid Tabu-Evolutionary algorithm, enabling efficient live migration to improve system resource utilization and reliability. His work directly supports the growing demand for intelligent, scalable industrial robot monitoring as IoT and cloud technologies expand. With his most-cited papers from 2021 and 2024, Wu’s research is gaining traction among engineers and researchers working on edge computing, industrial automation, and optimization algorithms. His innovative approaches to container migration and resource management are paving the way for more resilient and efficient industrial robotics systems, making his contributions increasingly relevant to both academia and industry.