Nagham Saeed

Work place: School of Computing and Engineering, University of West London, W5 5RF, United Kingdom



Research Interests: Mathematics of Computing, Computational Learning Theory, Artificial Intelligence


Dr. Nagham Saeed received her Ph.D. degree in optimised wireless communication networks from Brunel University, U.K., in 2012. She is currently a Senior Lecturer in Electrical Engineering with the School of Computer and Engineering, University of West London, UK. She has authored a respectable number of papers in journals and refereed conferences. Her current research interests include intelligent systems for modelling and classification to control industry operations or communication networks such as Mobile Ad Hoc NETworks (MANET) or Vehicular Ad hoc NETworks (VANETs) with the support of Artificial Intelligence (AI) , Machine learning (ML), and/or Industry Internet of Things (IIoT) in the process. She is a member Intelligent Sensing and Vision (IntSaV) and the Sensing, Localisation and Contextual Data Analytics (SENLOCDA) research groups.

Author Articles
IoT Leak Detection System for Building Hydronic Pipes

By Audrius Urbonavicius Nagham Saeed

DOI:, Pub. Date: 8 Sep. 2019

Building’s Air Conditioning systems require moving liquids for dweller comfort. Clogged pipes, system degradation can cause pressure buildups, leaks and other faults which leads to damage to the building. Most of the leaks in the commercial building occur due to poor maintenance and/or material degradation. Visual inspection is most predominantly used to solve this problem in the industry. This paper introduces the Internet of Things technology to detect leakage in building’s hydronic pipes with the support of sensors, fault detection method and mechanical control. The system consists of: Microcontroller, Windows application and website application. Internet of Things technology was used to monitor and control the hydronics using microcontroller’s capability of connecting to main server which is used to transmit the data to the cloud. The prototype was successfully built and tested. Promising results show that leaks above 2ml/s could be detected after 4 seconds specifically for the built small-scale system while control and monitor feature could be implemented with Internet of Things technology.

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