Time and Energy Savings in Leak Detection in WSN-Based Water Pipelines: A Novel Parametric Optimization-Based Approach

  • Muhammad MysorewalaEmail author


This paper presents a novel optimization algorithm for monitoring a complex water pipeline using Wireless Sensor Networks (WSN), in order to solve the trade-off between a timely and accurate detection of a leak, and an efficient utilization of the energy at the WSN’s nodes aimed at prolonging the WSN’s lifetime. The scheme relies on using vibration sensors of different sensitivities to detect vibrations due to a leak, and on exploiting duty-cycling, hierarchical adaptive sampling and wavelet-based signal compression, in order to reduce sensing, computation and communication energies. Given the constraints of a maximum allowable sensor energy, a limited time to detect a leak after it occurs, and an acceptable percentage of signal distortion due to compression, a new optimization-based backtracking learning algorithm is developed here that solves for the values of various monitoring parameters such that it satisfies all the given constraints. Developing such an optimization algorithm has also required performing a sensitivity analysis, i.e. investigating the effect of changing the key monitoring parameters on the performance of leak detection and energy consumption. Simulation results for various cases successfully demonstrate the effectiveness of the algorithm while supporting the prediction of the sensitivity analysis.


Adaptive sampling Energy efficiency Leak detection Optimization Vibration sensing Water pipeline Wavelet compression Wireless sensor network 



The author acknowledges the support provided by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum & Minerals (KFUPM) for funding this work through Project no. FT161017, and the numerous constructive discussions on this topic held with his team member Prof. L. Cheded.

Compliance with Ethical Standards

Conflict of Interest

There is no conflict of interest.


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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Systems Engineering DepartmentKing Fahd University of Petroleum and Minerals (KFUPM)DhahranSaudi Arabia

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