Achieving Smart Water Network Management Through Semantically Driven Cognitive Systems

  • Thomas BeachEmail author
  • Shaun Howell
  • Julia Terlet
  • Wanqing Zhao
  • Yacine Rezgui
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 534)


Achieving necessary resilience levels in urban water networks is a challenging proposition, with water network operators required to ensure a constant supply of treated water at pre-set pressure levels to a huge number of homes and businesses, all within strict budgetary restrictions. To achieve this, water network operators are required to overcome significant obstacles, including ageing assets within their infrastructure, the wide geographical area over which assets are spread, problematic internet connectivity in remote locations and a lack of interoperability between water network operator ICT systems. These issues act as key blockers for the deployment of smart water network management technologies such as optimisation, data driven modelling and dynamic water demand management. This paper presents how the use of a set cognitive analytic smart water components, underpinned by semantic modelling of the water network, can overcome these obstacles. The architecture and underpinning semantics of cognitive components are described along with how communication between these components is achieved. Two case studies are presented to demonstrate how the deployment of smart technologies can improve water network efficiency.


Smart water networks Semantics Resilience Cognitive systems 


  1. 1.
    Loucks, D.P., et al.: Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications. UNESCO, Paris (2005)Google Scholar
  2. 2.
    Kenny, D.: Smart Water Network Monitoring SBWWI Intelligent Networks. TaKaDu (2013)Google Scholar
  3. 3.
    Miller, J.M., Leinmiller, M.: Why Smart Water Networks Boost Efficiency. Schneider Electric (2014)Google Scholar
  4. 4.
    Howell, S., Rezgui, Y., Beach, T.: Water utility decision support through the semantic web of things. Environ. Model Softw. 102, 94–114 (2018)CrossRefGoogle Scholar
  5. 5.
    Barnaghi, P., Wang, W., Henson, C., Taylor, K.: Semantics for the internet of things: early progress and back to the future. Int. J. Semant. Web Inf. Syst. (IJSWIS) 81(1), 1–21 (2012)Google Scholar
  6. 6.
    Thomas, R.W., Friend, D.H., Dasilva, L.A., Mackenzie, A.B.: Cognitive networks: adaptation and learning to achieve end-to-end performance objectives. IEEE Commun. Mag. 44(12), 51–57 (2006)CrossRefGoogle Scholar
  7. 7.
    Dounis, A.I., Caraiscos, C.: Advanced control systems engineering for energy and comfort management in a building environment—a review. Renew. Sustain. Energy Rev. 13(6–7), 1246–1261 (2009)CrossRefGoogle Scholar
  8. 8.
    Vlacheas, P., et al.: Enabling smart cities through a cognitive management framework for the internet of things. IEEE Commun. Mag. 51(6), 102–111 (2013)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Thomas Beach
    • 1
    Email author
  • Shaun Howell
    • 1
  • Julia Terlet
    • 1
  • Wanqing Zhao
    • 1
  • Yacine Rezgui
    • 1
  1. 1.School of EngineeringCardiff UniversityCardiffUK

Personalised recommendations