Skip to main content
Erschienen in: Wireless Personal Communications 1/2017

15.06.2017

Outlier Detection in Wireless Sensor Networks Based on OPTICS Method for Events and Errors Identification

verfasst von: Aymen Abid, Atef Masmoudi, Abdennaceur Kachouri, Adel Mahfoudhi

Erschienen in: Wireless Personal Communications | Ausgabe 1/2017

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Wireless Sensor Network is composed of small, low cost, low energy, and multifunctional sensors. In addition, this network could have scalability, topology, synchronization, radio-coverage, safety and security constraints . Therefore, our challenge is to classify data into normal and abnormal measurements using outlier detection methods. This paper explore the density-based method Ordering Points to Identify the Clustering Structure. Proposed detector applies an auto- configuration of parameters without previous known environmental conditions. It also extracts hierarchical clusters that serve in a post-processing treatment for classification of data into errors and events. Performance is examined within a real and synthetic databases from Intel Berkeley Research lab. Results demonstrate that our proposed process analyzes data of this network with an average equal to 81% of outlier detection rate, 74% of precision rate and only 2% of false alarms rate that it is very low compared to other methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Abid, A., Kachouri, A., & Mahfoudhi, A. (2014). Assessment of anomalies detectors. In TWESD (Ed.), Tunisian workshop on embedded systems design. Sfax: CES. Abid, A., Kachouri, A., & Mahfoudhi, A. (2014). Assessment of anomalies detectors. In TWESD (Ed.), Tunisian workshop on embedded systems design. Sfax: CES.
2.
Zurück zum Zitat Andrea, K., Shevlyakov, G., Vassilieva, N., & Ulanov, A. (2014). A new measure of outlier detection performance. In D. Hutchison et al. (Eds.), Machine learning and data mining in pattern recognition (pp. 190–197). Cham (ZG): Springer. Andrea, K., Shevlyakov, G., Vassilieva, N., & Ulanov, A. (2014). A new measure of outlier detection performance. In D. Hutchison et al. (Eds.), Machine learning and data mining in pattern recognition (pp. 190–197). Cham (ZG): Springer.
3.
Zurück zum Zitat Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). Optics: Ordering points to identify the clustering structure. In S. B. Davidson & C. Faloutsos (Eds.), ACM sigmod record (Vol. 28, pp. 49–60). New York, NY: ACM. Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). Optics: Ordering points to identify the clustering structure. In S. B. Davidson & C. Faloutsos (Eds.), ACM sigmod record (Vol. 28, pp. 49–60). New York, NY: ACM.
4.
Zurück zum Zitat Bahrepour, M., Zhang, Y., Meratnia, N., & Havinga, P. J. (2009). Use of event detection approaches for outlier detection in wireless sensor networks. In 2009 5th International conference on intelligent sensors, sensor networks and information processing (ISSNIP) (pp. 439–444). IEEE. Bahrepour, M., Zhang, Y., Meratnia, N., & Havinga, P. J. (2009). Use of event detection approaches for outlier detection in wireless sensor networks. In 2009 5th International conference on intelligent sensors, sensor networks and information processing (ISSNIP) (pp. 439–444). IEEE.
5.
Zurück zum Zitat Barnett, V., & Lewis, T. (1994). Outliers in statistical data (Vol. 3). New York: Wiley.MATH Barnett, V., & Lewis, T. (1994). Outliers in statistical data (Vol. 3). New York: Wiley.MATH
6.
Zurück zum Zitat Branch, J. W., Giannella, C., Szymanski, B., Wolff, R., & Kargupta, H. (2013). In-network outlier detection in wireless sensor networks. Knowledge and Information Systems, 34(1), 23–54.CrossRef Branch, J. W., Giannella, C., Szymanski, B., Wolff, R., & Kargupta, H. (2013). In-network outlier detection in wireless sensor networks. Knowledge and Information Systems, 34(1), 23–54.CrossRef
7.
Zurück zum Zitat Chatzigiannakis, V., & Papavassiliou, S. (2007). Diagnosing anomalies and identifying faulty nodes in sensor networks. IEEE Sensors Journal, 7(5), 637–645.CrossRef Chatzigiannakis, V., & Papavassiliou, S. (2007). Diagnosing anomalies and identifying faulty nodes in sensor networks. IEEE Sensors Journal, 7(5), 637–645.CrossRef
8.
Zurück zum Zitat Chitradevi, N., Palanisamy, V., Baskaran, K., & Nisha, U. B. (2010). Outlier aware data aggregation in distributed wireless sensor network using robust principal component analysis. In 2010 International conference on computing communication and networking technologies (ICCCNT) (pp. 1–9). IEEE. Chitradevi, N., Palanisamy, V., Baskaran, K., & Nisha, U. B. (2010). Outlier aware data aggregation in distributed wireless sensor network using robust principal component analysis. In 2010 International conference on computing communication and networking technologies (ICCCNT) (pp. 1–9). IEEE.
9.
Zurück zum Zitat Chitradevi, N., Palanisamy, V., Baskaran, K., & Swathithya, K. (2013). Efficient density based techniques for anomalous data detection in wireless sensor networks. Journal of Applied Science and Engineering, 16(2), 211,223. Chitradevi, N., Palanisamy, V., Baskaran, K., & Swathithya, K. (2013). Efficient density based techniques for anomalous data detection in wireless sensor networks. Journal of Applied Science and Engineering, 16(2), 211,223.
10.
Zurück zum Zitat Daszykowski, M., Walczak, B., & Massart, D. L. (2002). Looking for natural patterns in analytical data. 2. Tracing local density with optics. Journal of Chemical Information and Computer Sciences, 42(3), 500–507.CrossRef Daszykowski, M., Walczak, B., & Massart, D. L. (2002). Looking for natural patterns in analytical data. 2. Tracing local density with optics. Journal of Chemical Information and Computer Sciences, 42(3), 500–507.CrossRef
11.
Zurück zum Zitat Duan, L. (2012). Density-based clustering and anomaly detection. In M. Mircea (Ed.), Business intelligence-solution for business development (pp. 79–96). Rijeka: INTECH Open Access Publisher. Duan, L. (2012). Density-based clustering and anomaly detection. In M. Mircea (Ed.), Business intelligence-solution for business development (pp. 79–96). Rijeka: INTECH Open Access Publisher.
12.
Zurück zum Zitat Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In E. Simoudis, J. Han & U. Fayyad (Eds.), Kdd (Vol. 96, pp. 226–231). Menlo Park, CA: AAAI Press. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In E. Simoudis, J. Han & U. Fayyad (Eds.), Kdd (Vol. 96, pp. 226–231). Menlo Park, CA: AAAI Press.
13.
Zurück zum Zitat Fawzy, A., Mokhtar, H. M., & Hegazy, O. (2013). Outliers detection and classification in wireless sensor networks. Egyptian Informatics Journal, 14(2), 157–164.CrossRef Fawzy, A., Mokhtar, H. M., & Hegazy, O. (2013). Outliers detection and classification in wireless sensor networks. Egyptian Informatics Journal, 14(2), 157–164.CrossRef
14.
Zurück zum Zitat GhasemiGol, M., Ghaemi-Bafghi, A., Yaghmaee-Moghaddam, M. H., & Sadoghi-Yazdi, H. (2014). Anomaly detection and foresight response strategy for wireless sensor networks. Wireless Networks, 21, 1–18. GhasemiGol, M., Ghaemi-Bafghi, A., Yaghmaee-Moghaddam, M. H., & Sadoghi-Yazdi, H. (2014). Anomaly detection and foresight response strategy for wireless sensor networks. Wireless Networks, 21, 1–18.
15.
Zurück zum Zitat Hara, S., Yomo, H., Popovski, P., & Hayashi, K. (2006). New paradigms in wireless communication systems. Wireless Personal Communications, 37(3–4), 233–241.CrossRef Hara, S., Yomo, H., Popovski, P., & Hayashi, K. (2006). New paradigms in wireless communication systems. Wireless Personal Communications, 37(3–4), 233–241.CrossRef
16.
Zurück zum Zitat Hassan, A., Mokhtar, H., & Hegazy, O. (2011). A heuristic approach for sensor network outlier detection. International Journal of Research and Reviews in Wireless Sensor Networks, 1(4), 66–72. Hassan, A., Mokhtar, H., & Hegazy, O. (2011). A heuristic approach for sensor network outlier detection. International Journal of Research and Reviews in Wireless Sensor Networks, 1(4), 66–72.
17.
Zurück zum Zitat Hawkins, D. (1980). Outliers from the linear model. In D. R. Cox, D. V. Hinkley, D. B. Rubin & B. W. Silverman (Eds.), Identification of outliers (pp. 85–103). Cham (ZG): Springer. Hawkins, D. (1980). Outliers from the linear model. In D. R. Cox, D. V. Hinkley, D. B. Rubin & B. W. Silverman (Eds.), Identification of outliers (pp. 85–103). Cham (ZG): Springer.
18.
Zurück zum Zitat Hinneburg, A., & Keim, D. A. (1998). An efficient approach to clustering in large multimedia databases with noise. In KDD (Vol. 98, pp. 58–65). Hinneburg, A., & Keim, D. A. (1998). An efficient approach to clustering in large multimedia databases with noise. In KDD (Vol. 98, pp. 58–65).
19.
Zurück zum Zitat Jolliffe, I. (2002). Principal component analysis. New York: Wiley Online Library.MATH Jolliffe, I. (2002). Principal component analysis. New York: Wiley Online Library.MATH
21.
Zurück zum Zitat Marsaglia, G., & Zaman, A. (1994). Some portable very-long-period random number generators. Computers in Physics, 8(1), 117–121.CrossRef Marsaglia, G., & Zaman, A. (1994). Some portable very-long-period random number generators. Computers in Physics, 8(1), 117–121.CrossRef
22.
Zurück zum Zitat Sluban, B., Gamberger, D., & Lavrač, N. (2014). Ensemble-based noise detection: Noise ranking and visual performance evaluation. Data Mining and Knowledge Discovery, 28(2), 265–303.CrossRefMATHMathSciNet Sluban, B., Gamberger, D., & Lavrač, N. (2014). Ensemble-based noise detection: Noise ranking and visual performance evaluation. Data Mining and Knowledge Discovery, 28(2), 265–303.CrossRefMATHMathSciNet
23.
Zurück zum Zitat Zhang, Y., Meratnia, N., & Havinga, P. (2010). Outlier detection techniques for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 12(2), 159–170.CrossRef Zhang, Y., Meratnia, N., & Havinga, P. (2010). Outlier detection techniques for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 12(2), 159–170.CrossRef
Metadaten
Titel
Outlier Detection in Wireless Sensor Networks Based on OPTICS Method for Events and Errors Identification
verfasst von
Aymen Abid
Atef Masmoudi
Abdennaceur Kachouri
Adel Mahfoudhi
Publikationsdatum
15.06.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 1/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4583-7

Weitere Artikel der Ausgabe 1/2017

Wireless Personal Communications 1/2017 Zur Ausgabe

Neuer Inhalt