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

TL;DR

This paper presents a simple, accurate model using gradient descent to predict received power in low-power IoT networks on water surfaces, aiding in environment monitoring under extreme conditions.

Contribution

It introduces a novel MMSE-based model for received power prediction that is computationally efficient and suitable for resource-constrained devices, with improved simplicity over traditional methods.

Findings

Achieves 91% prediction accuracy with few iterations

Uses gradient descent for parameter estimation

Effective in water surface IoT deployments

Abstract

Low-power and cost-effective IoT sensing nodes enable scalable monitoring of different environments. Some of these environments impose rough and extreme operating conditions, requiring continuous adaptation and reconfiguration of physical and link layer parameters. In this paper, we closely investigate the stability of the wireless links established between nodes deployed on the surface of different water bodies and propose a model to predict the received power. Our model is based on Minimum Mean Square Estimation (MMSE) and relies on the statistics of received power and the motion the nodes experience during communication. One of the drawbacks of MMSE is its reliance on matrix inversion, which is at once computationally expensive and difficult to implement with resource constrained devices. We forgo this stage by estimating model parameters using the gradient-descent approach, which is much simpler to implement. The model achieves a prediction accuracy of 91% even with a small number of iterations.