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

Zegeye Mekasha Kidane, Waltenegus Dargie

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.

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

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.
Paper Structure (18 sections, 22 equations, 5 figures, 5 tables)

This paper contains 18 sections, 22 equations, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Deployment
  4. Wireless Link Quality
  5. Transmit Power
  6. Large-Scale Path Loss
  7. Environment-Induced Variations
  8. Noise and Fast Fading
  9. Wide-Sense Stationary Property
  10. Modeling Outliers
  11. Variance-based selection of the EMA parameter
  12. Statistical Outlier Detection
  13. The Density Function of $\mathbf{z}_t$
  14. Results
  15. RSSI Dynamics and Outlier Patterns
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Prototype deployment in West Germany. From left to right: Sensor nodes in waterproof boxes before deployment. Deployment on a small lake (LK); deployment under Cologne Bridge (BG), along the bridge structure; and deployment on the Rhine River (RV).
  • Figure 2: Histograms of $\Delta RSSI$ for five different time windows (Radio: CC2538. Location: Rhine River).
  • Figure 3: The regions of interest to determine the probability distribution function of $\mathbf{z}_t$
  • Figure 4: Normalized RSSI variations in the 15 deployment environments using the four low-power radios. The red crosses mark the detected RSSI outliers.
  • Figure 5: Distribution of outlier detection rates for five anomaly detection methods across four radio platforms and fifteen environmental scenarios.