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

TL;DR

This paper presents a novel underwater sensor network system utilizing AI for security monitoring in drinking water reservoirs, capable of detecting, classifying, and localizing underwater events amidst complex environmental conditions.

Contribution

It introduces an innovative AI-based architecture with an autoencoder and sound localization for surveillance in challenging reservoir environments.

Findings

Successful laboratory and reservoir experiments demonstrate system effectiveness.

AI approach effectively detects and localizes underwater events.

System shows potential for real-world water reservoir security monitoring.

Abstract

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.