Adaptive edge analytics for distributed networked control of water systems


Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.

Publication DOI:
Divisions: Engineering & Applied Sciences
Engineering & Applied Sciences > Systems analytics research institute (SARI)
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Event Title: 2016 IEEE 1st International Conference on Internet-of-Things Design and Implementation
Event Type: Other
Event Dates: 2016-04-04 - 2016-04-08
Uncontrolled Keywords: IoT,cyber-physical systems,wireless sensor networks,anomaly detection,burst localization
ISBN: 978-1-4673-9948-7
Full Text Link:
Related URLs: http://ieeexplo ... cument/7471352/ (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2016-05-19
Accepted Date: 2016-01-01
Authors: Kartakis, Sokratis
Yu, Weiren
Akhavan, Reza
McCann, Julie



Version: Accepted Version

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