Using sparse representation to detect anomalies in complex WSNs

Xiaoming Li, Guangquan Xu, Xi Zheng, Kaitai Liang, Emmanouil Panaousis, Tao Li, Wei Wang, Chao Shen

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In recent years, wireless sensor networks (WSNs) have become an active area of research for monitoring physical and environmental conditions. Due to the interdependence of sensors, a functional anomaly in one sensor can cause a functional anomaly in another sensor, which can further lead to the malfunctioning of the entire sensor network. Existing research work has analysed faulty sensor anomalies but fails to show the effectiveness throughout the entire interdependent network system. In this article, a dictionary learning algorithm based on a non-negative constraint is developed, and a sparse representation anomaly node detection method for sensor networks is proposed based on the dictionary learning. Through experiment on a specific thermal power plant in China, we verify the robustness of our proposed method in detecting abnormal nodes against four state of the art approaches and proved our method is more robust. Furthermore, the experiments are conducted on the obtained abnormal nodes to prove the interdependence of multi-layer sensor networks and reveal the conditions and causes of a system crash.

Original languageEnglish
Article number64
Pages (from-to)1-18
Number of pages18
JournalACM Transactions on Intelligent Systems and Technology
Issue number6
Publication statusPublished - Oct 2019


  • Anomaly detection
  • Dependency relationships networks
  • Sparse Representation
  • WSNs


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