Prediction of the Power of Low-Power Networks Using Inertial Sensors

Energy-efficient Internet of Things (IoT) sensor nodes enable scalable monitoring of diverse physical environments, some of which are exposed to extreme and harsh operating conditions (such as heavy rain or strong movement). For reliable operation of such devices, certain variables must be adaptively adjusted. One of these variables is the transmission power of outgoing packets. In this work, we experimentally investigate how the movement of different bodies of water affects fluctuations in link quality and propose a model for predicting the received power. Once the received power is predicted, a transmitting node can adjust the transmission power to bring the received power to a desired level. Our model is based on minimum mean square estimation (MMSE) and leverages the received power statistics and the movement experienced by the nodes during communication. A disadvantage of MMSE is its dependence on matrix inversion, which is computationally intensive and difficult to implement on resource-constrained devices. We avoid this step and estimate the model parameters using gradient descent (GD), which is much easier to implement. The model achieves an average prediction accuracy of 91% (SD = 1.7%) even with a small number of iterations.