Rate My Professor David Murray

David Murray is an Associate Professor in the School of Information Technology at Murdoch University, where he also serves as Associate Dean of Teaching and Learning. His career at the university includes prior roles as Senior Lecturer, reflecting his longstanding commitment to advancing education and research in information technology. Murray's research interests span Internet of Things (IoT) technologies, wireless networks such as WiFi optimization including jumbo frames, LoRaWAN and low-power wide-area networks (LP-WANs), performance enhancing proxies, machine learning for localization and tracking, digital forensics, cybersecurity, and applications in precision agriculture, crop monitoring, insect detection, and bushfire detection using AI cameras.

He has made substantial contributions through supervision of multiple PhD theses, including Khondoker Ziaul Islam's 'Localization, Tracking, and Data Transmission Using LoRa' (2024), Abderraouf Amrani's 'Advances in deep learning techniques with uncertainty quantification for crop and insect detection' (2024), Butrus Mbimbi's blockchain-based IoT forensics research (2024), Joseph D. Stevens' IoT spectroscopic nutrient sensor development (related work 2023), Kevin Ong's 'Evaluation and optimisation of Less-than-Best-Effort TCP' (2020), Pema Choejey's 'Cybersecurity challenges and practices: A case study of government organizations' (2018), Nabhan Hamadneh's active queue management for TCP flows (2012), and others on topics like usability in high and low context cultures, nuclear power plant testbeds, and AI simulations. Key publications co-authored by Murray include 'Machine learning-based LoRa localisation using multiple gateways' (Islam et al., IET Wireless Sensor Systems, 2023), 'LoRa-based outdoor localization and tracking using machine learning' (Islam et al., 2024), 'Development and testing of an IoT spectroscopic nutrient sensor' (Stevens and Murray, Sensors, 2023), 'Insect detection from imagery using YOLOv3-based adaptive spatial feature extraction' (Amrani et al., 2023), 'Multi-task learning model for agricultural pest detection from UAV images' (Amrani et al., 2024), 'IoT Forensics-Based on the Integration of a Permissioned Blockchain' (Mbimbi et al., Blockchains, 2024), and 'Weighted RED (WTRED) Strategy for TCP Congestion Control' (Hamadneh et al., 2011). These works underscore his impact on networking performance, IoT security, and AI applications in agriculture and environmental management through collaborations with centers like the Centre for Crop and Food Innovation.