Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic WSN

Abstract Energy limitation is a major issue in WSN where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling approach based on spatio-temporal correlation of collected data and on nodes residual energy. This approach aims to optimize sampling rates of sensor nodes while ensuring a high quality of the collected data. In addition, a data reconstruction method based on linear regression is adopted in the sink to reconstruct the missing samples due to sampling rate reduction and adaptation compared to the case of a constant maximal sampling rate. We compare our approach with recently proposed adaptive sampling benchmark methods in different scenarios of data temporal correlation. Simulation results show the effectiveness of our proposed method in optimizing energy consumption by reducing sampling rate while maintaining data quality.