Wireless Sensor Network (WSN) applications that favor more local computations and less communication can contribute to solving the problem of high power consumption and performance issues plaguing most centralized WSN applications. In this study, we present a fully distributed solution, where leaks are detected in a water distribution network via only local collaborations between a sensor node and its close neighbors, without the need for long-distance transmissions via several hops to a centralized fusion center. A complete approach that includes the design, simulation, and physical measurements, showing how distributed computing implemented via a distributed Kalman filter improves the accuracy of leak detection and the power consumption is presented. The results from the physical implementation show that distributed data fusion increases the accuracy of leak detection while preserving WSN lifetime.

Water is a basic necessity and one of the most valuable resources for human living. Sadly, large quantities of treated water get lost daily worldwide, especially in developing countries, through leaks in the water distribution network. Wireless sensor network-based water pipeline monitoring (WWPM) systems using low-cost micro-electro-mechanical systems (MEMS) accelerometers have become popular for real-time leak detection due to their low-cost and low power consumption, but they are plagued with high false alarm rates. Recently, the distributed Kalman filter (DKF) has been shown to improve the leak detection reliability of WWPM systems using low-cost MEMS accelerometers. However, the question of which DKF is optimal in terms of leak detection reliability and energy consumption is still unanswered. This study evaluates and compares the leak detection reliability of three DKF algorithms, selected from distributed data fusion strategies based on diffusion, gossip and consensus. In this study, we used a combined approach involving simulations and laboratory experiments. The performance metrics used for the comparison include sensitivity, specificity and accuracy. The laboratory results revealed that the event-triggered diffusion-based DKF is optimal, having a sensitivity value of 61%, a specificity value of 93%, and an accuracy of 90%. It also has a lower communication burden and is less affected by packet loss, making it more responsive to real-time leak detection.