Energy Efficient Data Collection via Supervised In-Network Classification of Sensor Data
In wireless sensor networks, data collection (or gathering) is the task of transmitting rounds of measurements of physical phenomena from the sensor nodes to a sink node. We study how to increase the efficiency of data collection via supervised in-network classification of rounds of measurements. We assume that the end users of the data are interested only in rounds characterized by certain patterns. Hence the wireless sensor network uses classification to select the rounds of measurements that are transmitted to the base station. The energy consumption is potentially reduced by avoiding the transmission of rounds of measurements that are not of interest to the end users. In-network classification requires distributed feature extraction and transmission. Such tasks can be less or more energy expensive than the transmission of measurements without classification. We provide analytical results and simulations on real data to show requirements and key trade-offs for the design of in-network data classification systems that can improve the collection efficiency. Besides, we study the impact of spatial subsampling of the sensor data (a way to further decrease energy consumption) on the classification performance.