AquaSense AI: An Intelligent Multi-Sensor System for Water Network Monitoring, Leak Diagnosis, Localization, and Automated Control
Supervisor Name
Liana Qabajeh
Supervisor Email
liana_tamimi@ppu.edu
University
Palestine Polytechnic University
Research field
Computer Science
Bio
Liana Khamis Qabajeh was born in Hebron, Palestine, in 1977. She received her B.Sc. degree in Computer Systems Engineering from Palestine Polytechnic University (PPU), Hebron, Palestine in 2000 and joined the Engineering and Technology Faculty, PPU, as a research and teaching assistant. In 2003 she was awarded a DAAD-scholarship to continue her education in Jordan. She received her M.Sc. in Computer Engineering from Jordan University of Science and Technology, Irbid, Jordan in 2005. Between 2005 and 2008 before pursuing her study, she was primarily involved in academic teaching and research in PPU. In 2008 she was awarded an IDB-scholarship to attain her Ph.D. degree in Malaysia. She obtained her Ph.D. in Computer Science from University of Malaya, Kuala Lumpur, Malaysia in 2012. In 2012 she has been appointed as assistant professor in Information Technology and Computer Engineering Faculty, PPU. She has been appointed as Master of Informatics program coordinator at PPU during 2016-2019. Recently, she has been granted associate professor title and appointed as dean of Information Technology and Computer Engineering Faculty at PPU. Her research interests include distributed systems and wireless networks. She is particularly interested in security issues and Quality-of-Service (QoS) in Ad-Hoc networks routing protocols.
Description
The AquaSense AI project aims to develop an intelligent, integrated system for monitoring and managing water distribution networks by combining Internet of Things (IoT), embedded systems, Artificial Intelligence (AI), data analysis, and automated control. The system monitors network conditions, detects abnormal behavior, diagnoses problems, identifies affected zones, and supports automated control actions. The system uses multiple sensors, including flow, pressure, water-level, and acoustic/vibration sensors, to collect information from network points. ESP32 boards will operate as distributed sensing and control nodes, while a Raspberry Pi will serve as the central processing unit for data collection, processing, AI/ML execution, communication, and system coordination. MQTT will exchange sensor data and control commands between ESP32 nodes and the Raspberry Pi. A key component is AI-based multi-sensor data fusion. AI/ML models will analyze information from sensors for abnormal-condition detection, leak diagnosis, affected-zone localization, severity estimation, and predictive analysis. Based on the detected condition, the system can control solenoid valves in affected zones or adjust pump operation. Relay/driver modules will provide the required interface, while safety rules will help prevent unsafe actions. New measurements will then be collected to evaluate the result and verify whether the network condition has improved. The project is aligned with real-world challenges identified by Hebron Municipality in the water and wastewater sector, particularly leaks in some old networks and challenges related to water management and distribution. It can support future municipal and agricultural water-network applications.