Classification-based event detection in ecological monitoring networks

Abstract

Power-budgeting is a fundamental challenge in sensor networks today and the energy requirement of different sensing modalities is unevenly distributed. As a result, it is advisable to activate power-hungry sensors only during informative periods. Using low-power sensors, one can predict these informative periods due to strong correlations exhibited by environmental modalities. In this article, we consider an application of detecting “events” using classification based methods to increase the lifetime of the network. Specifically, we explore the problem of using low-power sensors to predict precipitation, which is one of the primary drivers of ecological activity. Such predictions can allow us to schedule the activation of expensive sensors (such as CO2) when they are most informative. In order to achieve this trade-off between power and collecting informative data, we focus our efforts on predicting/ classifying precipitation based on features extracted from inexpensive ambient temperature and barometric pressure modalities. Experimental results obtained from weather data collected over multiple years demonstrates that we can achieve accuracy towards 80% using these low-cost modalities and simple linear classifiers.