Papers
2
Total Citations
40
H-Index
2
About
Shao-You Wu is a researcher whose work lies at the intersection of wireless sensor networks, mobile robotics, and evolutionary computation. His primary research focus is on optimizing the path planning of data mules—mobile robots that traverse sensor networks to collect data from distributed nodes. This problem, inherently NP-hard, is central to extending the lifetime and efficiency of wireless sensor networks. Wu’s major contributions include developing an improved clustering-based genetic algorithm for data mule path planning, which achieved 28 citations for its novel approach to reducing travel distance. He further advanced the field by reframing the data collection problem as a Traveling Salesman Problem with Neighborhoods (TSPN), introducing shortcut-based evolutionary strategies to generate shorter, more efficient paths—a work cited 12 times. These contributions demonstrate Wu’s ability to apply sophisticated optimization techniques to real-world networking challenges, offering practical solutions for energy-constrained sensor systems. His research is particularly notable for bridging theoretical NP-hard problems with actionable robotic path planning, making his work valuable for students and researchers in robotics, wireless communications, and computational intelligence.
Research Focus
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Top Papers
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