We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks. We implement an innovative AI-based approach to detect, classify and localize underwater events. In this paper, we describe the technology and cognitive AI architecture of the system based on one of the sensor networks, the hydrophone network. We discuss the challenges of installing and using the hydrophone network in a water reservoir where traffic, visitors, and variable water conditions create a complex, varying environment. Our AI solution uses an autoencoder for unsupervised learning of latent encodings for classification and anomaly detection, and time delay estimates for sound localization. Finally, we present the results of experiments carried out in a laboratory pool and the water reservoir and discuss the system's potential.
@article{arxiv.2107.13977,
title = {Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs},
author = {Jörg Stork and Philip Wenzel and Severin Landwein and Maria-Elena Algorri and Martin Zaefferer and Wolfgang Kusch and Martin Staubach and Thomas Bartz-Beielstein and Hartmut Köhn and Hermann Dejager and Christian Wolf},
journal= {arXiv preprint arXiv:2107.13977},
year = {2021}
}