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

2026-03-22

AI Summary

This study addresses the challenge of maintaining both energy efficiency and link stability in low-power wireless sensor networks, where link quality is highly susceptible to environmental interference and hardware heterogeneity. Through a large-scale field deployment, the authors systematically evaluate the impact of 15 distinct physical environments and four types of heterogeneous low-power radios on link quality. They propose a lightweight, environment- and radio-specific statistical anomaly detection method that models temporal patterns in both raw and predicted RSSI (Received Signal Strength Indicator) sequences. By integrating empirical data across diverse environments and hardware platforms—a first in the field—the approach efficiently identifies link anomalies on resource-constrained devices, enabling tailored network configurations that significantly enhance system energy efficiency and reliability.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Outlier Detection
• Link Quality
• Low-Power Wireless Networks
• Environmental Factors
• Radio Heterogeneity

Innovation

• lightweight outlier detection
• link quality fluctuation
• low-power wireless networks
• RSSI statistics
• environment-specific characterization