The HKUST Frog Pond – A Case Study of Sensory Data Analysis

Abstract

Abstract. Many sensor network applications are data-centric, and data analysis plays an important role in these applications. However, it is a challenging task to find out what specific problems and requirements sensory data analysis will face, because these applications are tightly embedded in the physical world and the sensory data reflect the physical phenomena being monitored. In this paper, we propose to use field studies as an alternative for identifying these problems and requirements. Specifically, we deployed an experimental sensor network for monitoring the frog pond in our university and analyzed the collected sensory data. We present our methodology of sensory data collection and analysis. We also discuss preliminary analytical results from the collected sensory data, together with our generalization for similar sensor network applications. We find that this case study helped us identify and understand several problems, either general or specific, in real-world sensor network application deployment and sensory data analysis.

Pattern-based event detection in sensor networks

“…Our approach is based on a key observation that we obtained from various real-world sensory datasets: the occurrence of an event generates a particular spatio-temporal pattern in the sensor readings of the nodes that detect the event. This observation has been used in many previous studies [9,11,12,18,21,[28][29][30].…”

“…The sensor networks that we deployed to collect the two datasets are both single-hop networks. For FP, five motes were scattered in a pagoda surrounded by the frog pond [28]. For PL, six motes were deployed in the pervasive lab: two motes were attached to a swing model installed on the ceiling of the lab, two attached to the front edge of a desk and two on the lab door.…”