A lightweight Outlier Detection for Characterizing Radio- and Environment-Specific Link Quality Fluctuation in Low-Power Wireless Networks

TL;DR

This paper investigates how environmental and manufacturing factors affect link quality in low-power wireless networks, proposing a lightweight outlier detection method based on statistical analysis of RSSI across diverse environments.

Contribution

It introduces a novel, lightweight outlier detection technique tailored for characterizing link quality fluctuations in various physical environments and radio types.

Findings

External and internal factors significantly impact link quality.

The proposed outlier detection method effectively identifies quality fluctuations.

Results are validated across diverse environments and radio types.

Abstract

The performance of low-power wireless sensing networks can be influenced by both external environmental factors and internal imperfections which often arise due to manufacturing tolerance during mass production. Understanding the conditions and extent of these influences is important not only to achieve high performance and high energy efficiency, but also to carry our environment and radio specific configurations. In this paper we demonstrate, through extensive practical deployments and experiments, the extent to which external and internal factors affect the link quality of low-power wireless sensor networks. Moreover, we propose a lightweight statistical outlier detection technique and define all the parameter thereof in terms of the statistics of both the raw and the predicted link quality metrics (RSSI). Our study considers more than 15 different physical environments consisting of rivers, lakes, bridges, forests, and gardens, as well as four widely employed heterogeneous low-power radios.