Learning to Identify Malfunctioning Sensors in a Large-Scale Sensor Network

Tzu Heng Lin, Xin Ru Zhang, Chia Pan Chen, Jia Huei Chen, Hung Hsuan Chen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

This paper proposes a two-stage methodology to discover malfunctioning sensors in an air quality sensor network. The two-stage methodology consists of a supervised learner to predict the future PM2.5 values of each sensor and a detector that leverages the result of the previous stage to detect the malfunctioning sensors. Consequently, even if each sensor's health status (i.e., normal or malfunctioning) is unavailable, we can still apply powerful supervised learners to this task. We conduct experiments on a nationwide air quality sensor network that includes 10,000+ sensors and utilize periodic maintenance records on some of these sensors as the ground truth of their health status. Experimental results show that this two-stage methodology can effectively discover problematic sensors. As maintaining a large-scale sensor network is laborious, the methodology can dramatically reduce the human resource required for regular inspection.

Original languageEnglish
Pages (from-to)2582-2590
Number of pages9
JournalIEEE Sensors Journal
Volume22
Issue number3
DOIs
StatePublished - 1 Feb 2022

Keywords

  • AIoT
  • Auto inspection
  • IoT
  • PM25
  • anomaly detection
  • sensor network

Fingerprint

Dive into the research topics of 'Learning to Identify Malfunctioning Sensors in a Large-Scale Sensor Network'. Together they form a unique fingerprint.

Cite this