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

机译：基于Kalman滤波器的无线传感器网络需求传输功率控制（AODTPC）算法

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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.
机译：传输功率控制（TPC）是节省资源受限无线传感器网络（WSN）中传感器节点的能量的关键技术。已经提出了各种算法来增强网络的寿命。然而，由于无线衰落通道的不确定行为，在时变传播环境中的传感器节点的功率级调节仍然需要深度调查。为了解决这个问题，本文提出了根据传播环境变化的节能且可靠的功率控制算法。我们提出了一种众所周知的算法的自适应版本，按需传输功率控制（ODTPC），命名为Adaptive ODTPC或AODTPC。所提出的算法基于卡尔曼滤波器，该滤波器用于通过结合时变衰落信道条件来预测未来接收的无线电信号强度指示符（RSSI）值。然后使用这些值在数据传输之前在ODTPC策略的帮助下调节传输功率水平。因此，这项工作的主要目的是捕获不确定环境的时变差，并根据现实环境行为调整功率水平。仿真结果表明，AODTPC在能效方面执行更好，并增加节点寿命而不是其前身。

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