RF sensing with dense IoT network graphs: An EM-informed analysis

TL;DR

This paper presents a theoretical and empirical analysis of RF sensing in dense IoT networks, using an EM model and graph neural networks to predict and detect human presence and movement, with bounds on accuracy and resolution.

Contribution

It introduces an EM-informed framework for analyzing RF sensing limits and proposes a deep graph neural network for movement detection in dense IoT networks.

Findings

Theoretical bounds on the number of distinguishable subjects in RF sensing.

Deep graph neural network effectively detects human movement.

Model predictions align with indoor case study results.

Abstract

Radio Frequency (RF) sensing is attracting interest in research, standardization, and industry, especially for its potential in Internet of Things (IoT) applications. By leveraging the properties of the ElectroMagnetic (EM) waves used in wireless networks, RF sensing captures environmental information such as the presence and movement of people and objects, enabling passive localization and vision applications. This paper investigates the theoretical bounds on accuracy and resolution for RF sensing systems within dense networks. It employs an EM model to predict the effects of body blockage in various scenarios. To detect human movements, the paper proposes a deep graph neural network, trained on Received Signal Strength (RSS) samples generated from the EM model. These samples are structured as dense graphs, with nodes representing antennas and edges as radio links. Focusing on the problem of identifying the number of human subjects co-present in a monitored area over time, the paper analyzes the theoretical limits on the number of distinguishable subjects, exploring how these limits depend on factors such as the number of radio links, the size of the monitored area and the subjects physical dimensions. These bounds enable the prediction of the system performance during network pre-deployment stages. The paper also presents the results of an indoor case study, which demonstrate the effectiveness of the approach and confirm the model's predictive potential in the network design stages.