DECOR: Distributed and Energy Efficient Collection of Raw Data in Sensor Networks

Abstract

In wireless sensor networks, collection of raw sensor data at a base station provides the flexibility to perform offline detailed analysis on the data which may not be possible with in-network data aggregation. However, lossless data collection consumes considerable amount of energy for communication while sensors usually have limited energy. In this paper, we propose a Distributed and Energy Efficient algorithm for collection of Raw data in sensor networks called DECOR. DECOR exploits spatial correlation to reduce the communication energy in sensor networks with highly correlated data. In our approach, at each neighborhood, one sensor shares its raw data as a reference with the rest of sensors without any suppression or compression. Other sensors use this reference data to compress their observations by representing them in the forms of mutual differences. In a highly correlated network, transmission of reference data consumes significantly more energy than transmission of compressed data. Thus, we first attempt to minimize the number of reference transmissions. Then, we try to minimize the size of mutual differences. We derive analytical lower bounds for both these phases and based on our theoretical results, we propose a two-step distributed data collection algorithm which reduces the communication energy significantly compared to existing methods. In addition, we modify our algorithm for lossy communication channels and we evaluate its performance through simulation.