Abstract:

Recent technological advances, especially in smart homes and buildings, have driven the need for Cognitive cities to develop intelligent infrastructure for efficient resource management, like water. A key approach involves designing practical pipeline networks integrated with other water distribution components. These networks face challenges from natural factors such as temperature changes, pipe fractures, and sudden pressure surges. Pressure demand fluctuations from usage also complicate management, making system optimization critical to avoid catastrophic failures.
This study proposes improved water management solutions by surveying the potential of wireless sensor networks (WSNs), artificial intelligence (AI), and Internet of Things (IoT) technologies. It goes beyond traditional methods by exploring AI-driven techniques like pressure optimization, intelligent pump scheduling, and valve control to minimize water loss.
The integration of IoT wireless sensors and protocols like LoRaWAN and WirelessHART is reviewed for efficient, low-power data collection in pipeline networks, offering advantages over traditional SCADA systems. Simulation tools like EPANET for hydraulic modelling and ns-3 for network simulations are highlighted, along with Python-based AI frameworks.
The concept of digital twins for real-world deployment through The Things Network (TTN) and microcontroller gateways is discussed.
Several industrial use cases and pilot projects are examined to showcase practical advancements. Comparative analyses of different technologies, scalability studies for urban LoRaWAN deployments, and discussions on challenges such as data quality, security, and integration are presented.
Finally, the study emphasizes the innovation and feasibility of a fully simulated AI-IoT water pipeline management system, setting the stage for future sustainable smart city developments.

Speaker Bio:

Mr. Fawaz Babajide Aremu is currently pursuing a Master of Science in Systems and Controls Engineering at King Fahd University of Petroleum and Minerals (KFUPM), within the Department of Control and Instrumentation Systems Engineering. His research focuses on AI-Driven Smart Water Pipeline Networks for Cognitive Cities, with an emphasis on enhancing water distribution efficiency, sustainability, and resilience through artificial intelligence, Internet of Things (IoT) technologies, and digital twin simulations.
Fawaz holds a First Class B.Eng. degree in Mechatronics Engineering from Afe Babalola University, Nigeria. He brings over three years of practical experience in industrial automation, having worked on projects involving Siemens PCS 7 DCS systems, PLC programming, HMI/SCADA development, and control systems integration for industrial applications.
His research interests include AI-enabled predictive maintenance, IoT-based intelligent monitoring systems, pressure optimization, and the development of intelligent control strategies for urban infrastructure systems. He is certified in PLC, SCADA, and HMI programming (Siemens, Allen Bradley, ABB, Delta) and holds additional certifications from City & Guilds London and Honeywell Academy.
Through his academic and professional endeavours, Fawaz aims to contribute to the development of sustainable, intelligent urban infrastructure and the next generation of smart water management systems