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research-article

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

Authors:

Sabu M. Thampi,

Jaime LloretAuthors Info & Claims

Mobile Networks and Applications, Volume 25, Issue 1

Pages 55 - 67

https://doi.org/10.1007/s11036-018-1192-y

Published: 01 February 2020 Publication History

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.

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Cited By

Saha SArya R(2022)ARCMTComputer Communications10.1016/j.comcom.2022.07.016193:C(246-265)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.comcom.2022.07.016
Misra SOjha TP M(2021)SecRETACM Transactions on Autonomous and Adaptive Systems10.1145/343139015:1(1-26)Online publication date: 20-Jan-2021
https://dl.acm.org/doi/10.1145/3431390

Index Terms

Anomaly Detection in UASN Localization Based on Time Series Analysis and Fuzzy Logic
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Mobile Networks and Applications

Mobile Networks and Applications Volume 25, Issue 1

Feb 2020

364 pages

ISSN:1383-469X

Issue’s Table of Contents

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

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 February 2020

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Cited By

Saha SArya R(2022)ARCMTComputer Communications10.1016/j.comcom.2022.07.016193:C(246-265)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.comcom.2022.07.016
Misra SOjha TP M(2021)SecRETACM Transactions on Autonomous and Adaptive Systems10.1145/343139015:1(1-26)Online publication date: 20-Jan-2021
https://dl.acm.org/doi/10.1145/3431390

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