Prediction of Received Power in Low-Power Networks Deployed on the Surface of Rough Waters

2025-02-19

AI Summary

To address link instability and unpredictable received power caused by wave-induced motion in low-power IoT nodes operating over rough water surfaces, this paper proposes a lightweight motion-aware received power prediction model tailored for embedded devices. Methodologically, it replaces the high-complexity matrix inversion in conventional MMSE estimation with online gradient descent for channel parameter estimation; furthermore, it introduces the first joint modeling of surface-node motion statistics—such as pitch and roll distributions—with multipath fading, enabling a time-aware received power prediction framework. Experimental results demonstrate 91% prediction accuracy with minimal iterations, while computational overhead is reduced by two orders of magnitude—enabling real-time, adaptive communication on resource-constrained IoT nodes. Key contributions include: (i) a novel motion-channel joint modeling paradigm; (ii) a low-complexity online parameter estimation mechanism; and (iii) a lightweight prediction architecture specifically designed for dynamic aquatic environments.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Predict received power in low-power networks
• Model stability for wireless links on water
• Simplify MMSE with gradient-descent approach

Innovation

• Minimum Mean Square Estimation
• Gradient-descent approach
• Received power prediction model