Performance comparison of Kalman filter based approaches for energy efficiency in wireless sensor networks

Summary form only given. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In this paper, we study a performance comparison of two recently proposed link adaptation techniques based on Kalman filter at MAC layer to enhance energy efficiency of the sensor nodes in mobile wireless sensor networks. These two new approaches use extended Kalman filter (EKF) and unscented Kalman filter (UKF) to predict the optimal frame size for improving energy efficiency and goodput, while minimizing the sensor memory requirement. We designed and verified different network models to evaluate and analyze the proposed link adaptation schemes. The simulation results show that the UKF-based link adaptation algorithm offers a better performance than the EKF-based algorithm due to less errors on estimation and prediction in a nonGaussian nonlinear scenario.