Papers
3
Total Citations
118
H-Index
3
About
Weili Wu is a leading researcher in wireless sensor networks, with a focus on optimizing data collection using mobile elements like robots and vehicles. Her major contributions center on minimizing data collection latency—a critical challenge in environmental monitoring and IoT systems. In her highly cited 2013 paper (59 citations), she pioneered the study of multiple data MULE trajectory planning, defining the k-traveling salesman problem variant to compute optimal paths that reduce latency. Her 2012 work (53 citations) further advanced this by jointly optimizing mobile element trajectories and sensor communication powers, enabling energy-efficient, long-term monitoring. Wu’s research bridges theoretical optimization with practical deployment, offering scalable solutions for large-scale sensor networks. Her notable achievements include developing algorithms that balance latency, energy, and mobility constraints, directly impacting applications like precision agriculture and disaster response. With over 100 total citations, her work is foundational for researchers exploring mobile data collection, trajectory planning, and network lifetime extension. Wu’s clear problem formulations and rigorous proofs make her a key figure in advancing wireless sensor network efficiency.
Research Focus
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