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Anomaly Detection in UASN Localization Based on Time Series Analysis and Fuzzy Logic

  • Anjana P. DasEmail author
  • Sabu M. Thampi
  • Jaime Lloret
Article
  • 59 Downloads

Abstract

Underwater acoustic sensor network (UASN) offers a promising solution for exploring underwater resources remotely. For getting a better understanding of sensed data, accurate localization is essential. As the UASN acoustic channel is open and the environment is hostile, the risk of malicious activities is very high, particularly in time-critical military applications. Since the location estimation with false data ends up in wrong positioning, it is necessary to identify and ignore such data to ensure data integrity. Therefore, in this paper, we propose a novel anomaly detection system for UASN localization. To minimize computational power and storage, we designed separate anomaly detection schemes for sensor nodes and anchor nodes. We propose an auto-regressive prediction-based scheme for detecting anomalies at sensor nodes. For anchor nodes, a fuzzy inference system is designed to identify the presence of anomalous behavior. The detection schemes are implemented at every node for enabling identification of multiple and duplicate anomalies at its origin. We simulated the network, modeled anomalies and analyzed the performance of detection schemes at anchor nodes and sensor nodes. The results indicate that anomaly detection systems offer an acceptable accuracy with high true positive rate and F-Score.

Keywords

Underwater sensor networks Localization Time series analysis Anomaly detection Fuzzy logic Auto-regression 

Notes

Acknowledgements

The authors thank the Higher Education Department, Government of Kerala, for the research fellowship.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of KeralaKeralaIndia
  2. 2.Indian Institute of Information Technology and Management-KeralaKeralaIndia
  3. 3.Universidad Politecnica de ValenciaValènciaSpain

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