Tiong Hoo Lim
Papers
2
Total Citations
10
H-Index
2
About
Tiong Hoo Lim is a researcher whose work bridges artificial intelligence, robotics, and the Internet of Things (IoT), with a particular focus on autonomous systems and environmental monitoring. His key contributions lie in developing intelligent algorithms for self-learning robots and advancing IoT-based solutions for aquaculture. Notably, his 2021 paper on the "Recursive Backtracking Depth-First Search Algorithm in Unknown Search Space for Self-learning Path Finding Robot" (6 citations) introduces a novel approach to autonomous navigation, enabling robots to efficiently explore and map unfamiliar environments without prior knowledge—a critical capability for search-and-rescue and industrial automation. More recently, his 2024 survey on "IoT-Based Underwater Robotics for Water Quality Monitoring in Aquaculture" (4 citations) synthesizes emerging technologies for real-time, remote sensing of aquatic ecosystems, addressing pressing challenges in sustainable food production. Though his citation counts are modest, Lim’s work is notable for its practical, problem-driven focus, directly tackling real-world constraints like unknown search spaces and resource-limited IoT deployments. His research offers valuable insights for students and engineers interested in the intersection of robotics, sensor networks, and environmental intelligence.
Research Focus
Key Achievements
Top Papers
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- 2