Papers
2
Total Citations
38
H-Index
2
About
Ko-Ming Chiu is a researcher whose work sits at the intersection of wireless sensor networks (WSNs), mobile robotics, and optimization algorithms. His primary research focus is on solving complex routing and path planning problems for data-collecting mobile robots—often called "data mules"—within WSNs. Chiu’s major contribution lies in developing advanced genetic algorithm (GA) implementations to tackle the NP-hard challenge of generating the shortest possible path for a robot to gather data from all sensor nodes. His most-cited work, "Path planning of a data mule in wireless sensor network using an improved implementation of clustering-based genetic algorithm" (2013), has garnered 28 citations, demonstrating its influence in the field. In a related study, "Robot routing using clustering-based parallel genetic algorithm with migration" (2011), he introduced a parallel GA with migration strategies to enhance routing efficiency, earning 10 citations. Chiu’s innovative clustering-based approaches have advanced the practical deployment of mobile robots in environmental monitoring, healthcare, and space exploration, making his research a valuable resource for students and engineers seeking efficient, scalable solutions for autonomous data collection in constrained networks.
Research Focus
Key Achievements
Top Papers
- 1
- 2