Shuo Shi
Papers
3
Total Citations
10
H-Index
2
About
Shuo Shi is a researcher specializing in wireless sensor networks (WSNs), mobile data gathering, and trajectory optimization for smart city applications. His work centers on solving complex routing and scheduling challenges for mobile elements — including robots, vehicles, and aircraft — operating within large-scale sensor network environments. Shi's most notable contribution lies in developing heuristic and optimization-based algorithms that address the multiple Traveling Salesman Problem (mTSP) as applied to WSNs. His 2020 paper on heuristic mobile data gathering introduces a trajectory control framework designed to simultaneously minimize travel distance, balance energy consumption, and reduce data latency — a particularly impactful contribution garnering 6 citations. His earlier 2018 works introduced the EmCA clustering algorithm and an mTSP with Communication Range (mTSP-CR) approach, both targeting energy efficiency in large-scale deployments by intelligently leveraging sensor communication ranges. Collectively, these contributions reflect Shi's commitment to bridging theoretical optimization with practical, real-world constraints in IoT and smart city infrastructure. While his citation profile is still emerging, his body of work offers meaningful algorithmic foundations for researchers tackling energy-aware, multi-agent data collection problems in next-generation sensor networks.
Research Focus
Key Achievements
Top Papers
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