Analysis of the impact of data correlation on adaptive sampling in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are often densely deployed to monitor a physical phenomenon, whose nature often exhibits temporal correlation in sequential readings. Such a dense deployment results in high correlation of sensing data in the space domain. Since WSNs suffer from sever resource constraints, temporal, spatial and spatio-temporal correlation among sensor data can be exploited to find an optimal sampling strategy, which reduces the number of sampling nodes and/or sampling rates while maintaining high data quality. In this study, we investigate the impact of the data correlation on sampling strategies, by taking both data quality and energy consumption into account.