STEP: Semantics-Aware Sensor Placement for Monitoring Community-Scale Infrastructure

Abstract

Built utility infrastructures provide essential services such as water, gas, and power to communities, and their resilient operation under anomalies and spurious events is critical. In this paper, we study the deployment of heterogeneous IoT sensors in geo-distributed infrastructure networks, using stormwater as a driving usecase. These systems are responsible for drainage and flood control, but in doing so, serve as conduits that carry pollutants to receiving waters. The timely detection of such events is challenging, due to the transient/random nature of pollutants, scarce historical data, and complexity of the system. We present STEP, an integrated framework for sensor placement that leverages the network structure and topology, behavioral properties (e.g., flow rate), and community semantics such as locations of facilities (e.g., commercial spaces, residential areas, and industrial plants, etc.). We identify key metrics to capture anomaly coverage and traceability, use past pollution incidents to inform sensor deployment, and model network operations through physics-based simulations and community-scale semantics. STEP is evaluated on six real-world stormwater networks, which show the efficacy of our approach over existing methods.