Drift estimation using pairwise slope with minimum variance in wireless sensor networks

This paper proposes a new slope estimation strategy for linear regression to be used by flooding based time synchronization protocols. The proposed method, namely Pairwise Slope With Minimum Variance (PSMV), calculates the slope of the estimated regression line by considering the pairwise slope between the earliest and the most recently collected data points. The PSMV slope is less affected by the large errors on the received data, i.e. it is more stable, and it is more computationally efficient when compared to the slope of the least-squares line. We incorporated PSMV into two flooding based time synchronization protocols, namely Flooding Time Synchronization Protocol (FTSP) and PulseSync. Experimental results collected from a testbed setup including 20 sensor nodes show that PSMV strategy improves the performance of FTSP by a factor of 4 and preserves the performance of PulseSync in terms of synchronization error with 40% less CPU overhead for linear regression. Our simulations show that these results also hold for networks with larger diameters and densities.