Energy-Efficient Sensor Data Collection Approach for Industrial Process Monitoring

The use of wireless sensor network for industrial applications has attracted much attention from both academic and industrial sectors. It enables a continuous monitoring, controlling, and analyzing of the industrial processes, and contributes significantly to finding the best performance of operations. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. Since the sensors are resource constrained, effective energy management should include new data collection techniques for an efficient utilization of the sensors. In this paper, we propose adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. We provide and compare three different data collection techniques. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, whereas the second one is based on the set-similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes were performed in order to evaluate the performance of our techniques. The obtained results proved that our proposed adaptive data collection methods can reduce the number of acquired samples up to 80% with respect to a traditional fixed-rate technique. Furthermore, our experimental results showed significant energy savings and high accurate data collection compared to existing approaches.