Skip to main content

Binary Search Based PSO for Master Node Enumeration and Placement in a Smart Water Metering Network

  • Conference paper
  • First Online:

Abstract

A Binary Search based Particle Swarm Optimization (BS-PSO) algorithm is proposed for the enumeration and placement of Master Nodes (MNs) in a Smart Water Metering Network (SWMN). The merit of this proposal is that it can simultaneously optimize the number of MNs as well as their locations in the SWMN. The Binary Search (BS) Mechanism searches a pre-specified range of integers for the optimal number of MNs. This algorithm iteratively invokes the PSO algorithm which generates particles based on the chosen number of MNs. The PSO uses these particles to determine MN coordinates in the fitness function evaluation process within the underlying SWMN simulation. The packet delivery ratio (PDR) is designated as the fitness value for the particle. Results for 10 BS-PSO optimization runs show that the median optimal number of MNs is 15 and that the mean PDR of 96% can be realized. As part of future work, more optimization runs will be conducted to enhance the generalization of the results. The extension of this concept to other optimization algorithms such as Differential Evolution will also be considered.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Cahn, A.: An overview of smart water networks. J. Am. Water Works Assoc. 106(7), 68–74 (2014)

    Article  Google Scholar 

  2. Beach, T., Howell, S., Terlet, J., Zhao, W., Rezgui, Y.: Achieving smart water network management through semantically driven cognitive systems. In: Camarinha-Matos, L.M., Afsarmanesh, H., Rezgui, Y. (eds.) PRO-VE 2018. IAICT, vol. 534, pp. 478–485. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99127-6_41

    Chapter  Google Scholar 

  3. de Azevedo, M.T., Martins, A.B., Kofuji, S.T.: Digital transformation in the utilities industry: industry 4.0 and the smart network water. In: Technological Developments in Industry 4.0 for Business Applications, pp. 304–330. IGI Global (2019)

    Google Scholar 

  4. Malcolm, F., Gary, W., Zainuddin, G.: The Manager’s Non-revenue water Handbook - A Guide to Understanding Water Losses. USAID, USA (2008)

    Google Scholar 

  5. Sensus Research: WATER 20/20: Bringing smart water network into focus. Sensus, North American Headquarters (2012)

    Google Scholar 

  6. Shitumbapo, L.N., Nyirenda, C.N.: Simulation of a smart water metering network in Tsumeb East, Namibia. In: International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), pp. 44–49 (2015)

    Google Scholar 

  7. Nyirenda, C.N., Nyandowe, I., Shitumbapo, L.: A comparison of the collection tree protocol (CTP) and AODV routing protocols for a Smart Water Metering Network in Tsumeb, Namibia. In: IST-Africa Week Conference, pp. 1–8 (2016)

    Google Scholar 

  8. McNabb, J.: Vulnerabilities of wireless water meter networks. J. New Engl. Water Works Assoc. 126(1), 31–37 (2012)

    Google Scholar 

  9. Albentia, S.: Proposal for Smart Metering Networks Solution, ALB-W012-000en, UK (2012)

    Google Scholar 

  10. Nyirenda, C.N., Makwara, P., Shitumbapo, L.: Particle swarm optimization based placement of data acquisition points in a smart water metering network. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2016. LNNS, vol. 16, pp. 905–916. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-56991-8_66

    Chapter  Google Scholar 

  11. Mudumbe, J.M., Adnan, M., Abu-Mahfouz, M.: Smart Water Meter System for user-centric consumption measurement. In: 13th International Conference on industrial Informatics (INDIN 2015) (2015)

    Google Scholar 

  12. Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8_630

    Chapter  Google Scholar 

  13. Spinsante, S., Pizzichini, M., Mencarelli, M., Squartini, S., Gambi, E.: Evaluation of the wireless M-Bus standard for future smart water grids. In: 9th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 382–1387 (2013)

    Google Scholar 

  14. Levis, P., Lee, N., Welsh, M., Culler, D.: TOSSIM: accurate and scalable simulation of entire TinyOS applications. In: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, pp. 126–137 (2003)

    Google Scholar 

  15. Fonseca, R., Gnawali, O., Jamieson, K., Kim, S., Levis, P., Woo, A.: The collection tree protocol (CTP). TinyOS TEP 123(2) (2006)

    Google Scholar 

  16. Zuniga, M.: Building a network topology for Tossim. USC Technical Report (2011)

    Google Scholar 

  17. Takahama, T.: PSO code. http://www.ints.info.hiroshima-cu.ac.jp/~takahama/download/PSO.html. Accessed 19 July 2019

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Clement N. Nyirenda .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nyirenda, C.N., Nyirongo, S.G. (2020). Binary Search Based PSO for Master Node Enumeration and Placement in a Smart Water Metering Network. In: Zitouni, R., Agueh, M., Houngue, P., Soude, H. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-030-41593-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-41593-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41592-1

  • Online ISBN: 978-3-030-41593-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics