A lightweight Outlier Detection for Characterizing Radio- and Environment-Specific Link Quality Fluctuation in Low-Power Wireless Networks
This work addresses performance and energy efficiency issues for low-power wireless sensing networks, but it is incremental as it builds on existing outlier detection methods with specific parameter definitions.
The paper tackled the problem of link quality fluctuations in low-power wireless networks caused by environmental and internal factors, proposing a lightweight outlier detection technique and demonstrating through deployments in over 15 environments and four radio types that these factors significantly affect performance.
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.