A Kalman filter based adaptive on demand transmission power control (AODTPC) algorithm for wireless sensor networks

Transmission power control (TPC) is a key technique to save the energy of a sensor node in a resource-constrained wireless sensor network (WSN). A variety of algorithms have been proposed to enhance the lifetime of the network. Nevertheless, Power-level regulation of a sensor node in time-varying propagation environment still needs deep investigation due to the uncertain behavior of the wireless fading channel. In order to address this issue, an energy efficient and reliable power control algorithm that works according to the variations in the propagation environment is presented in this paper. We propose an adaptive version of a well known algorithm, On Demand Transmission Power Control (ODTPC), named as Adaptive ODTPC or AODTPC. The proposed algorithm is based on Kalman Filter, which is used to predict the future received radio signal strength indicator (RSSI) values by incorporating the time-varying fading channel conditions. These values are then used to regulate the transmission power level with the help of ODTPC strategy prior to data transmission. Thus, the main objective of this work is to capture the time-varying variations of uncertain environment and adjust the power levels according to realistic environment behavior. Simulation results demonstrate that AODTPC performs better in terms of energy efficiency and increases node lifetime than its predecessor.