Model-Based Event Detection in Wireless Sensor Networks
Jayant Gupchup, Andreas Terzis, Randal Burns, Alex Szalay

TL;DR
This paper introduces a PCA-based method for detecting environmental events in wireless sensor networks by modeling normal patterns and identifying deviations, demonstrated by rain event detection using temperature data.
Contribution
It applies PCA for event detection in sensor networks, capturing seasonal trends and detecting discrete events through divergence analysis, which is a novel application in this context.
Findings
Successfully detected rain onset using temperature data
Model captures daily and seasonal environmental trends
Divergence method effectively identifies discrete events
Abstract
In this paper we present an application of techniques from statistical signal processing to the problem of event detection in wireless sensor networks used for environmental monitoring. The proposed approach uses the well-established Principal Component Analysis (PCA) technique to build a compact model of the observed phenomena that is able to capture daily and seasonal trends in the collected measurements. We then use the divergence between actual measurements and model predictions to detect the existence of discrete events within the collected data streams. Our preliminary results show that this event detection mechanism is sensitive enough to detect the onset of rain events using the temperature modality of a wireless sensor network.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Chemical Sensor Technologies · Energy Efficient Wireless Sensor Networks · Distributed Sensor Networks and Detection Algorithms
