Energy-Efficient Sensor Data Acquisition Based on Periodic Patterns

Wireless sensor networks have received considerable attention in recent years and played an important role in data collection applications. Sensor nodes usually have limited supply of energy. Therefore, a major consideration for developing sensor network applications is to conserve the energy for sensor nodes. In this paper, we propose a novel energy-efficient data acquisition algorithm based on the periodic patterns derived from past sensor readings. Our key observation is that sensor readings often exhibit periodic patterns, e.g., the daily cycle of temperature readings, and the patterns provide opportunities for reducing energy consumption for sensor data acquisition. We exploit the patterns and use the patterns to build a statistic model for predicting sensor readings. In our approach, sensor data acquisition is needed only when acquired readings are unpredictable. Therefore the energy for sensor data acquisition and the associated radio communications can be conserved. The experiments performed with real data validate the effectiveness and efficiency of our approach.