Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3018896.3065829acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccConference Proceedingsconference-collections
research-article

A copula based approach for measurement validity verification in wireless sensor networks

Published: 22 March 2017 Publication History

Abstract

Outlier detection is the process of identifying the data objects that do not comply with the normal behavior of the defined data model. Used in automated data analysis, it ensures the desired data quality and reliability. This field has attracted increasing attention in the wireless sensor network domain, using methods from machine learning, data mining, and statistics. In this paper, we propose a novel outlier detection approach based on Copula theory. This powerful theory allows to model the dependency between data measurements in a formal and statistical way. We have evaluated our proposed approach with a real world dataset. Our results show a detection rate of 85.90% and an error rate of 0.87%.

References

[1]
Guillermo Barrenetxea, François Ingelrest, Gunnar Schaefer, and Martin Vetterli. 2008. The hitchhiker's guide to successful wireless sensor network deployments. In Proceedings of the 6th ACM conference on Embedded network sensor systems. ACM, 43--56.
[2]
Luís MA Bettencourt, Aric A Hagberg, and Levi B Larkey. 2007. Separating the wheat from the chaff: practical anomaly detection schemes in ecological applications of distributed sensor networks. In International Conference on Distributed Computing in Sensor Systems. Springer, 223--239.
[3]
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. 2000. LOF: identifying density-based local outliers. In ACM sigmod record, Vol. 29. ACM, 93--104.
[4]
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. ACM computing surveys (CSUR) 41, 3 (2009), 15.
[5]
Fiona Edwards-Murphy, Michele Magno, Pádraig M Whelan, John O'Halloran, and Emanuel M Popovici. 2016. b+ WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring. Computers and Electronics in Agriculture 124 (2016), 211--219.
[6]
Christian Genest and Jock MacKay. 1986. The joy of copulas: bivariate distributions with uniform marginals. The American Statistician 40, 4 (1986), 280--283.
[7]
Mohammad Hammoudeh, Robert Newman, Christopher Dennett, and Sarah Mount. 2013. Interpolation techniques for building a continuous map from discrete wireless sensor network data. Wireless Communications and Mobile Computing 13, 9 (2013), 809--827.
[8]
Mohammad Hammoudeh, Robert Newman, Christopher Dennett, Sarah Mount, and Omar Aldabbas. 2015. Map as a Service: A Framework for Visualising and Maximising Information Return from Multi-Modal Wireless Sensor Networks. Sensors 15, 9 (2015), 22970--23003.
[9]
Mohammad Hammoudeh, Robert Newman, and Sarah Mount. 2009. An approach to data extraction and visualisation for wireless sensor networks. In Networks, 2009. ICN'09. Eighth International Conference on. IEEE, 156--161.
[10]
Simon Hawkins, Hongxing He, Graham Williams, and Rohan Baxter. 2002. Outlier detection using replicator neural networks. In International Conference on Data Warehousing and Knowledge Discovery. Springer, 170--180.
[11]
François Ingelrest, Guillermo Barrenetxea, Gunnar Schaefer, Martin Vetterli, Olivier Couach, and Marc Parlange. 2010. SensorScope: Application-specific sensor network for environmental monitoring. ACM Transactions on Sensor Networks (TOSN) 6, 2 (2010), 17.
[12]
Rashmi Jain, Shaunak Kulkarni, Ahtesham Shaikh, and Akash Sood. 2016. AUTOMATIC IRRIGATION SYSTEM FOR AGRICULTURE FIELD USING WIRELESS SENSOR NETWORK (WSN). (2016).
[13]
Richard Arnold Johnson, Dean W Wichern, and others. 2002. Applied multivariate statistical analysis. Vol. 5. Prentice hall Upper Saddle River, NJ.
[14]
Kirk Martinez, Paritosh Padhy, Alistair Riddoch, Royan Ong, and Jane Hart. 2005. Glacial environment monitoring using sensor networks. In Proceedings of the Workshop on Real-World Wireless Sensor Networks (REALWSN'05), Stockholm, Sweden.
[15]
Themistoklis Palpanas, Dimitris Papadopoulos, Vana Kalogeraki, and Dimitrios Gunopulos. 2003. Distributed deviation detection in sensor networks. ACM SIGMOD Record 32, 4 (2003), 77--82.
[16]
Osman Salem, Alexey Guerassimov, Ahmed Mehaoua, Anthony Marcus, and Borko Furht. 2016. Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models. (2016).
[17]
Bo Sheng, Qun Li, Weizhen Mao, and Wen Jin. 2007. Outlier detection in sensor networks. In Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing. ACM, 219--228.
[18]
Robert Szewczyk, Joseph Polastre, Alan Mainwaring, and David Culler. 2004. Lessons from a sensor network expedition. In European Workshop on Wireless Sensor Networks. Springer, 307--322.
[19]
Chafiq Titouna, Makhlouf Aliouat, and Mourad Gueroui. 2015. Outlier detection approach using bayes classifiers in wireless sensor networks. Wireless Personal Communications 85, 3 (2015), 1009--1023.
[20]
Gilman Tolle and David E Culler. 2005. Design of an application-cooperative management system for wireless sensor networks. In EWSN, Vol. 5. 121--132.
[21]
Gilman Tolle, Joseph Polastre, Robert Szewczyk, David Culler, Neil Turner, Kevin Tu, Stephen Burgess, Todd Dawson, Phil Buonadonna, David Gay, and others. 2005. A macroscope in the redwoods. In Proceedings of the 3rd international conference on Embedded networked sensor systems. ACM, 51--63.
[22]
Pravin K Trivedi and David M Zimmer. 2007. Copula modeling: an introduction for practitioners. Now Publishers Inc.
[23]
S Vasuhi and V Vaidehi. 2016. Target tracking using Interactive Multiple Model for Wireless Sensor Network. Information Fusion 27 (2016), 41--53.
[24]
Juan J Villacorta, María I Jiménez, Lara del Val, and Alberto Izquierdo. 2011. A configurable sensor network applied to ambient assisted living. Sensors 11, 11 (2011), 10724--10737.
[25]
Weili Wu, Xiuzhen Cheng, Min Ding, Kai Xing, Fang Liu, and Ping Deng. 2007. Localized outlying and boundary data detection in sensor networks. IEEE Transactions on knowledge and data engineering 19, 8 (2007), 1145--1157.
[26]
Yang Zhang, Nirvana Meratnia, and Paul JM Havinga. 2009. Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks. In GSN. Springer, 31--41.

Cited By

View all
  • (2021)Design of Four Elements UWB MIMO Antenna System with On Demand Band Notch Functionality2021 1st International Conference on Microwave, Antennas & Circuits (ICMAC)10.1109/ICMAC54080.2021.9678282(1-4)Online publication date: 21-Dec-2021

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing
March 2017
1349 pages
ISBN:9781450347747
DOI:10.1145/3018896
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. WSN
  2. copula
  3. dependency
  4. outlier
  5. reliability
  6. statistical

Qualifiers

  • Research-article

Conference

ICC '17

Acceptance Rates

ICC '17 Paper Acceptance Rate 213 of 590 submissions, 36%;
Overall Acceptance Rate 213 of 590 submissions, 36%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2021)Design of Four Elements UWB MIMO Antenna System with On Demand Band Notch Functionality2021 1st International Conference on Microwave, Antennas & Circuits (ICMAC)10.1109/ICMAC54080.2021.9678282(1-4)Online publication date: 21-Dec-2021

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media