Underwater Acoustic Networks for Security Risk Assessment in Public
  Drinking Water Reservoirs
  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.
