Transmission power control using state estimation-based received signal strength prediction for energy efficiency in wireless sensor networks

Authors : RAMAKRISHNAN SABITHA, KRISHNA BHUMA, THANGAVELU THYAGARAJAN

Pages : 591-604

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Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :In battery-operated wireless sensor networks, the quality of service requirements such as throughput and energy efficiency must be stringently maintained while the least amount of energy is consumed. Since a major portion of energy is spent by the transceiver operations, transmission power control insert ignore into journalissuearticles values(TPC); in the medium access control insert ignore into journalissuearticles values(MAC); layer can bring about considerable energy efficiency. Since TPC algorithms will have a direct impact on the received signal strength index insert ignore into journalissuearticles values(RSSI); at the receiver, RSSI is the primary input parameter for any TPC algorithm. The objective of the proposed work is to decide on the exact value of transmission power required for the next transmission that will ensure an RSSI just above the threshold level at the receiver. Since this involves estimation of RSSI for the next transmission, we propose three state estimation techniques, namely Kalman filter insert ignore into journalissuearticles values(KF);, extended KF insert ignore into journalissuearticles values(EKF);, and unscented KF insert ignore into journalissuearticles values(UKF); to predict the RSSI accurately. This predicted value is used as an input for an artificial neural network insert ignore into journalissuearticles values(ANN);-based TPC algorithm. The effectiveness of the estimation techniques is verified by the prediction error. The accuracy of prediction is reflected in the TPC algorithm in terms of reduced power utilization.
Keywords : Wireless sensor networks, RSSI prediction, state estimation techniques, transmission power control, Kalman filter, neural network controller