Imperial College London


Faculty of EngineeringDepartment of Computing

Professor of Computer Systems



+44 (0)20 7594 8375j.mccann Website




Miss Teresa Ng +44 (0)20 7594 8300




260ACE ExtensionSouth Kensington Campus






BibTex format

author = {Kartakis, S and Yu, W and Akhavan, M and McCann, J},
doi = {10.1109/IoTDI.2015.34},
pages = {72--82},
publisher = {IEEE},
title = {Adaptive edge analytics for distributed networked control of water systems},
url = {},
year = {2016}

RIS format (EndNote, RefMan)

AB - 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.
AU - Kartakis,S
AU - Yu,W
AU - Akhavan,M
AU - McCann,J
DO - 10.1109/IoTDI.2015.34
EP - 82
PY - 2016///
SP - 72
TI - Adaptive edge analytics for distributed networked control of water systems
UR -
UR -
ER -