Rate My Professor Wee Lum Tan

Wee Lum Tan is a Senior Lecturer in the School of Information and Communication Technology within Griffith Sciences at Griffith University. His research specializes in wireless networks and mobile systems, covering areas such as software-defined networking, drive-thru internet performance modeling, 3G/4G network capacity evaluation, wireless mesh networks, IoT device fingerprinting, hidden camera detection in Wi-Fi networks, and blockchain applications for supply chain traceability. Previously associated with National ICT Australia (NICTA), Tan contributed to formal verification of routing protocols like AODV and empirical studies on network performance. He has authored or co-authored numerous papers presented at prestigious conferences and published in journals, demonstrating substantial impact with over 1,100 citations on ResearchGate.

Key publications include the highly cited "Blockchain based Wine Supply Chain Traceability System" (2017, cited by 332), "Efficient topology discovery in software defined networks" (2014, 158 citations), "Analytical models and performance evaluation of drive-thru internet systems" (2010, 146 citations), "Efficient topology discovery in OpenFlow-based software defined networks" (2016, 131 citations), "An empirical study on the capacity and performance of 3G networks" (2008, 112 citations), and "A process algebra for wireless mesh networks" (2012, 96 citations). Recent works feature "A comprehensive review of IoT device fingerprinting" (2025, with Mariam Munsif Mir and Mohammad Awrangjeb), "IoTCommFreq: Efficient IoT Device Fingerprinting Through Network Behaviour Analysis Using Communication Frequencies" (2025), "A computer vision enhanced IoT system for koala monitoring and recognition" (2025, with J. Trevathan and others), "Detection and Localization of Hidden Wi-Fi Cameras" (2022, with R. Cunningham), and "Performance Comparison of WhatsApp versus Skype on 3G Networks" (2018, with N. Patel and S. Patel). At Griffith, Tan serves as course convenor for computing subjects, including digital logic for novice students using physical gates, and supervises PhD and research students on IoT, computer vision, and machine learning projects for applications like koala conservation and adversarial attacks in wireless networks. His contributions advance both theoretical modeling and real-world deployments in networking and interdisciplinary fields.