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Artificial Intelligence Review

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01.02.2015

Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey

verfasst von: Nauman Shahid, Ijaz Haider Naqvi, Saad Bin Qaisar

Erschienen in: Artificial Intelligence Review | Ausgabe 2/2015

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Abstract

Wireless sensor networks (WSNs) have received considerable attention for multiple types of applications. In particular, outlier detection in WSNs has been an area of vast interest. Outlier detection becomes even more important for the applications involving harsh environments, however, it has not received extensive treatment in the literature. The identification of outliers in WSNs can be used for filtration of false data, find faulty nodes and discover events of interest. This paper presents a survey of the essential characteristics for the analysis of outlier detection techniques in harsh environments. These characteristics include, input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types(local and global), type of approach(distributed/centralized), outlier identification(event or error), outlier degree, outlier score, susceptibility to dynamic topology, non-stationarity and inhomogeneity. Moreover, the prioritization of various characteristics has been discussed for outlier detection techniques in harsh environments. The paper also gives a brief overview of the classification strategies for outlier detection techniques in WSNs and discusses the feasibility of various types of techniques for WSNs deployed in harsh environments.

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Abe S (2010) Support vector machines for pattern classification. Springer, New YorkCrossRefMATH

Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38:393–422CrossRef

Akyildiz IF, Akan zgr B, Akan OB, Chen C, Fang J, Su W (2003) Interplanetary internet: state-of-the-art and research challenges. Comput Netw 43:75–112CrossRefMATH

Aly M (2005) Survey on multiclass classification methods. Neural Netw 1–9

Bahrepour M, Meratnia N, Havinga PJM (2008) Automatic fire detection: a survey from wireless sensor network perspective. Centre for Telematics and Information Technology University of Twente, Enschede, technical report TR-CTIT-08-73, Dec 2008. http://eprints.eemcs.utwente.nl/14624/

Bahrepour M, Meratnia N, Havinga PJM (2009a) Sensor fusion-based event detection in wireless sensor networks. In: SensorFusion, Toronto, Canada. IEEE, Los Alamitos, pp 1–8

Bahrepour M, Meratnia N, Havinga PJM (2009b) Use of ai techniques for residential fire detection in wireless sensor networks. In: AIAI 2009 workshop proceedings, Greece, vol 475, July 2009, pp 311–321. ceur-ws.org

Bahrepour M, Zhang Y, Meratnia N, Havinga PJM (2009c) Use of event detection approaches for outlier detection in wireless sensor networks. In: Proceedings of symposium on theoretical and practical aspects of large-scale wireless sensor networks, the 5th international conference on intelligent sensors, sensor networks and information processing 2009 (ISSNIP 2009), Melbourne, Australia. IEEE Press, Victoria, Dec 2009, pp 439–444

Bahrepour M, Meratnia N, Havinga PJM (2010a) Fast and accurate residential fire detection using wireless sensor networks. Environ Eng Manag J 9(2):215–221

Bahrepour M, Meratnia N, Poel M, Taghikhaki Z, Havinga PJM (2010b) Distributed event detection in wireless sensor networks for disaster management. In: International conference on intelligent networking and collaborative systems, INCoS 2010, Thessaloniki, Greece. IEEE Computer Society, USA, pp 507–512

Bahrepour M, van der Zwaag BJ, Meratnia N, Havinga P JM (2010c) Fire data analysis and feature reduction using computational intelligence methods. In: Phillips-Wren G, Jain LC, Nakamatsu K (eds) Proceedings of the second KES international symposium on advances in intelligent decision technologies, IDT 2010, Baltimore, Maryland, USA, series smart innovation, systems and technologies, vol 4. Springer, Berlin/Heidelberg, July 2010, pp 289–298

Barnett V, Lewis T (1994) Outliers in statistical data. Wiley, LononMATH

Bettencourt LMA, Hagberg AA, Larkey LB (2007) Separating the wheat from the chaff: practical anomaly detection schemes in ecological applications of distributed sensor networks. In: Computing distributed in sensor systems (DCOSS 2007), Santa Fe, NM, USA, June 2007, pp 223–239

Bezdek J, Havens T, Keller J, Leckie C, Park L, Palaniswami M, Rajasegarar S (2010) Clustering elliptical anomalies in sensor networks. In: 2010 IEEE international conference on fuzzy systems (FUZZ), pp 1–8

Bezdek J, Rajasegarar S, Moshtaghi M, Leckie C, Palaniswami M, Havens T (2011) Anomaly detection in environmental monitoring networks [application notes]. Comput Intell Mag IEEE 6(2):52–58

Bhuse V, Gupta A (2006) Anomaly intrusion detection in wireless sensor networks. J High Speed Netw 15:33–51

Branch J, Szymanski B, Giannella C, Wolff R, and Kargupta H (2006) In-network outlier detection in wireless sensor networks. In: 26th IEEE international conference on distributed computing systems, 2006. ICDCS 2006, p 51

Cardell-Olivera R, Kranza M, Smettemb K, Mayerc K (2005) A reactive soil moisture sensor network: design and field evaluation. Int J Distrib Sens Netw 1(2):149–162CrossRef

Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41:15:1–15:58. doi:10.1145/1541880.1541882

Ch V, Banerjee A, Kumar V, Chandola V (2007) Outlier detection: a survey

Chen J, Kher S, Somani A (2006) Distributed fault detection of wireless sensor networks. In: Proceedings of the 2006 workshop on dependability issues in wireless ad hoc networks and sensor networks, series DIWANS ’06. ACM, New York, NY, pp 65–72. doi: 10.1145/1160972.1160985

Chintalapudi K, Govindan R (2003) Localized edge detection in sensor fields. In: Proceedings of the first IEEE 2003 international workshop on sensor network protocols and applications, May 2003, pp 59–70

da Silva APR, Martins MHT, Rocha BPS, Loureiro AAF, Ruiz LB, Wong HC (2005) Decentralized intrusion detection in wireless sensor networks. In Proceedings of the 1st ACM international workshop on quality of service & security in wireless and mobile networks, series Q2SWinet ’05. ACM, New York, NY, pp 16–23. doi: 10.1145/1089761.1089765

Dario IA, Akyildiz IF, Pompili D, Melodia T (2005) Underwater acoustic sensor networks: research challenges. Ad Hoc Netw 3:257–279CrossRef

Dereszynski E, Dietterich T (2011) Spatiotemporal models for data-anomaly detection in dynamic environmental monitoring campaigns. ACM Trans Sens Netw 8(1):3CrossRef

Ding M, Cheng X (2009) Robust event boundary detection in sensor networks—a mixture model based approach. In: IEEE INFOCOM 2009, April 2009, pp 2991–2995

Ding M, Chen D, Xing K, Cheng X (2005) Localized fault-tolerant event boundary detection in sensor networks. In: Proceedings IEEE of 24th annual joint conference of the IEEE computer and communications societies INFOCOM 2005, vol 2, pp 902–913

Ekström J (2011) Mahalanobis distance beyond normal distributions. UCLA Stat (preprint)

Elnahrawy E, Nath B (2004) Context-aware sensors. In: European workshop on wireless sensor, networks, pp 77–93

Ganguly AR (2008) Knowledge discovery from sensor data. CRC Press, Boca RatonCrossRef

Garca-Hernndez CF, Ibargengoytia-Gonzlez PH, Garca-Hernndez J, PrezDaz JA (2004) Wireless sensor networks and applications: a survey. Int J Comput Sci Netw Secur 7(3):264–273

George S, Zhou W, Chenji H, Won M, Lee Y, Pazarloglou A, Stoleru R, Barooah P (2010) Distressnet: a wireless ad hoc and sensor network architecture for situation management in disaster response. IEEE Commun Mag 48(3):128–136CrossRef

Giatrakos N, Kotidis Y, Deligiannakis A (2010a) Pao: power-efficient attribution of outliers in wireless sensor networks. In: Proceedings of the seventh international workshop on data management for sensor networks. ACM, pp 33–38

Giatrakos N, Kotidis Y, Deligiannakis A, Vassalos V, Theodoridis Y (2010b) Taco: tunable approximate computation of outliers in wireless sensor networks. In: Proceedings of the 2010 international conference on management of data. ACM, pp 279–290

Gomez-Verdejo V, Arenas-Garcia J, Lazaro-Gredilla M, Navia-Vazquez A (2011) Adaptive one-class support vector machine. IEEE Trans Signal Process 59(6):2975–2981CrossRefMathSciNet

Gupta P, Kumar P (2000) The capacity of wireless networks. IEEE Trans Inf Theory 46(2):388–404CrossRefMATHMathSciNet

Han J, Kamber M (2006) Data mining: concepts and techniques. Morgan Kaufmann, Los Altos

Hao P, Chiang J, Lin Y (2009) A new maximal-margin spherical-structured multi-class support vector machine. Appl Intell 30(2):98–111CrossRef

Hassan A, et al (2011) A heuristic approach for sensor network outlier detection. Int J Res Rev Wirel Sens Netw 1(4):66–72

Hill DJ, Minsker BS, Amir E (2007) Real-time bayesian anomaly detection for environmental sensor data. In: Proceedings of the 32nd conference of IAHR, 2007

Hodge VJ, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 22:85–126. doi:10.1007/s10462-004-4304-y

http://connekt.seecs.nust.edu.pk/SAHSE.php

http://www.genuki.org.uk/big/eng/LAN/Haydock/WoodPitExplosion.html

http://www.humanite.fr/2006-03-10_Societe_-Catastrophe-de-Courrieres-une-expression-impropre

http://www.msha.gov/MSHAINFO/FactSheets/MSHAFCT8.HTM

Janakiram D, Adi Mallikarjuna Reddy V, Phani Kumar A (2006) Outlier detection in wireless sensor networks using bayesian belief networks. In: Communication system software and middleware, 2006. Comsware 2006, pp 1–6

John GH (1995) Robust decision trees: removing outliers from databases. In: In knowledge discovery and data mining. AAAI Press, Menlo Park, pp 174–179

Jun MC, Jeong H, Kuo C-CJ (2005) Distributed spatio-temporal outlier detection in sensor networks

Keally M, Zhou G, Xing G (2010) Watchdog: confident event detection in heterogeneous sensor networks. In: 2010 16th IEEE on real-time and embedded technology and applications symposium (RTAS), pp 279–288

Keerthi S, Sundararajan S, Chang K, Hsieh C, Lin C (2008) A sequential dual method for large scale multi-class linear svms. In: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 408–416

Knorr EM, Ng RT (1988) Algorithms for mining distance-based outliers in large datasets, pp 392–403

Krishnamachari B, Iyengar S (2004) Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans Comput 53(3):241–250CrossRef

Lazarevic A, Ozgur A, Ertoz L, Srivastava J, Kumar V (2003) A comparative study of anomaly detection schemes in network intrusion detection. In: Proceedings of the third SIAM international conference on data mining

Liu S, Liu Y, Wang B (2007) An improved hyper-sphere support vector machine. In: Third international conference on natural computation, 2007. ICNC 2007, vol 1. IEEE, pp 497–500

Liu C, Yang Y, Tang C (2010) An improved method for multi-class support vector machines. In: 2010 International conference on measuring technology and mechatronics automation (ICMTMA), vol 1, pp 504–508

Luo X, Dong M, Huang Y (2006) On distributed fault-tolerant detection in wireless sensor networks. IEEE Trans Comput 55(1):58–70

Madden S, Franklin MJ, Hellerstein JM, Hong W (2002) Tag: a tiny aggregation service for ad-hoc sensor networks. In: IN OSDI, 2002

Mainwaring A, Culler D, Polastre J, Szewczyk R, Anderson J (2002) Wireless sensor networks for habitat monitoring. In: Proceedings of the 1st ACM international workshop on wireless sensor networks and applications, series WSNA ’02. ACM, New York, NY, pp 88–97. doi: 10.1145/570738.570751

Misra P, Kanhere S, Ostry D, Jha S (2010) Safety assurance and rescue communication systems in high-stress environments: a mining case study. Commun Mag IEEE 48(4):66–73CrossRef

Moshtaghi M, Havens T, Bezdek J, Park L, Leckie C, Rajasegarar S, Keller J, Palaniswami M (2011a) Clustering ellipses for anomaly detection. Pattern Recog 44(1):55–69CrossRefMATH

Moshtaghi M, Leckie C, Karunasekera S, Bezdek J, Rajasegarar S, Palaniswami M (2011b) Incremental elliptical boundary estimation for anomaly detection in wireless sensor networks. In: 2011 IEEE 11th international conference on data mining (ICDM), pp 467–476

Moshtaghi M, Rajasegarar S, Leckie C, Karunasekera S (2011c) An efficient hyperellipsoidal clustering algorithm for resource-constrained environments. Pattern Recog 44:2197–2209

Ni L, Liu Y, Lau YC, Patil A (2003) Landmarc: indoor location sensing using active rfid. In: Proceedings of the first IEEE international conference on pervasive computing and communications, 2003 (PerCom 2003), March 2003, pp 407–415

Ozdemir S, Xiao Y (2011) Outlier detection based fault tolerant data aggregation for wireless sensor networks. In: 2011 5th IEEE international conference on application of information and communication technologies, pp 1–5

Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2003) Distributed deviation detection in sensor networks. SIGMOD Rec 32:77–82. doi:10.1145/959060.959074

Phua C, Lee V, Smith K, Gayler R (2010) A comprehensive survey of data mining-based fraud detection research. Arxiv, preprint arXiv:1009.6119

Rajasegarar S, Leckie C, Palaniswami M, Bezdek JC (2006) Distributed anomaly detection in wireless sensor networks. In: 10th IEEE Singapore international conference on communication systems, 2006. ICCS 2006, Oct 2006, pp 1–5

Rajasegarar S, Leckie C, Palaniswami M, Bezdek J (2007) Quarter sphere based distributed anomaly detection in wireless sensor networks. In: IEEE international conference on communications. ICC ’07, June 2007, pp 3864–3869

Rajasegarar S, Leckie C, Palaniswami M (2008a) Anomaly detection in wireless sensor networks. IEEE Wirel Commun 15(4):34–40CrossRef

Rajasegarar S, Leckie C, Palaniswami M (2008b) Cesvm: centered hyperellipsoidal support vector machine based anomaly detection. In: IEEE international conference on communications, 2008. ICC ’08, May 2008, pp 1610–1614

Rajasegarar S, Leckie C, Bezdek J, Palaniswami M (2010a) Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Trans Inf Forensic Secur 5(3):518–533CrossRef

Rajasegarar S, Bezdek JC, Leckie C, Palaniswami M (2010b) Elliptical anomalies in wireless sensor networks. ACM Trans Sens Netw 6:7:1–7:28 [Online].10.1145/1653760.1653767

Rajasegarar S, Bezdek J, Moshtaghi M, Leckie C, Havens T, Palaniswami M (2012) Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids. In: The 2012 international joint conference on IEEE in neural networks (IJCNN), pp 1–8

Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. SIGMOD Rec 29:427–438. doi:10.1145/335191.335437

Ross G, Tasoulis D, Adams N (2009) Online annotation and prediction for regime switching data streams. In: Proceedings of the 2009 ACM symposium on applied computing, pp 1501–1505

Rousseeuw P, Leroy A (1996) Robust regression and outlier detection. Wiley, London

Schieferdecker D, Völker M, Wagner D (2011) Efficient algorithms for distributed detection of holes and boundaries in wireless networks. Exp Algorithm 6630:388–399

Shahid N, Naqvi IH (2011) Energy efficient outlier detection in wsns based on temporal and attribute correlations. In: International conference on emerging technologies, 2011

Shahid N, Naqvi IH, Qaisar SB (2012a) Quarter-sphere SVM: attribute and spatio-temporal correlations based outlier & event detection in wireless sensor networks. In: 2012 IEEE wireless communications and networking conference: mobile and wireless networks (IEEE WCNC 2012 track 3 mobile & wireless), France, Paris

Shahid N, Naqvi IH, Qaisar SB (2012b) Real time energy efficient approach to outlier & event detection in wireless sensor networks. In: 13th IEEE international conference on communication systems 2012 (IEEE ICCS’12), Singapore, Singapore

Sharma A, Golubchik L, Govindan R (2010) Sensor faults: detection methods and prevalence in real-world datasets. ACM Trans Sens Netw 6(3):23CrossRef

Sheng B, Li Q, Mao W, jin W (2007) Outlier detection in sensor networks

Shnayder V, Hempstead M, rong Chen B, Allen GW, Welsh M (2004) Simulating the power consumption of large-scale sensor network applications. In: In Sensys. ACM Press, pp 188–200

Somorjai R, Dolenko B, Nikulin A, Roberson W, Thiessen N (2011) Class proximity measures-dissimilarity-based classification and display of high-dimensional data. J Biomed Inf 44(5):775–788

Subramaniam S, Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2006) Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd international conference on very large data bases, series VLDB ’06. VLDB endowment, pp 187–198. Available on http://portal.acm.org/citation.cfm?id=1182635.1164145

Suthaharan S, Alzahrani M, Rajasegarar S, Leckie C, Palaniswami M (2010a) Labelled data collection for anomaly detection in wireless sensor networks. In: 2010 sixth international conference on intelligent sensors, sensor networks and information processing (ISSNIP), Dec 2010, pp 269–274

Suthaharan S, Leckie C, Moshtaghi M, Karunasekera S, Rajasegarar S (2010b) Sensor data boundary estimation for anomaly detection in wireless sensor networks. In: 2010 IEEE 7th international conference on mobile adhoc and sensor systems (MASS), pp 546–551

Tan P, Steinback M, Kumar V (2006) Introduction to data mining. Addison Wesley, Reading

Tax DMJ, Duin RPW (1999) Data domain description using support vectors. In: ESANN’99, pp 251–256

Tutorial on wireless communications and electronic tracking, 2009

Wang D, Yeung DS, Tsang ECC (2006) Structured one-class classification. IEEE Trans Syst Man Cybern Part B Cybern 36(6):1283–1295CrossRef

Wu W, Cheng X, Ding M, Xing K, Liu F, Deng P (2007) Localized outlying and boundary data detection in sensor networks. IEEE Trans Knowl Data Eng 19(8):1145–1157

Xu T (2009) A new sphere-structure multi-class classifier. In: Pacific-Asia conference on circuits, communications and systems, 2009. PACCS’09. IEEE, pp 520–525

Xu T, He D, Luo Y (2007) A new orientation for multi-class svm. In: Eighth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, 2007. SNPD 2007, vol 3. IEEE, pp 899–904

Xue W, Luo Q, Chen L, Liu Y (2006) Contour map matching for event detection in sensor networks. In: Proceedings of the 2006 ACM SIGMOD international conference on management of data, series SIGMOD ’06. ACM, New York, NY, 2006, pp 145–156. doi:10.1145/1142473.1142491

Yang Z, Meratnia N, Havinga P (2008) An online outlier detection technique for wireless sensor networks using unsupervised quarter-sphere support vector machine. In: International conference on intelligent sensors, sensor networks and information processing, 2008. ISSNIP 2008, pp 151–156

Yozo CP, Hida Y, Huang P, Nishtala R (2004) Aggregation query under uncertainty in sensor networks, technical report

Zhang Y (2010) Observing the unobservable—distributed online outlier detection in wireless sensor networks. Ph.D. dissertation, University of Twente

Zhang Y, Meratnia N, Havinga PJM (2007a) A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets. Centre for Telematics and Information Technology University of Twente, Enschede, technical report TR-CTIT-07-79, Nov 2007. http://eprints.eemcs.utwente.nl/11366/

Zhang K, Shi S, Gao H, Li J, (2007b) Unsupervised outlier detection in sensor networks using aggregation tree. In: Proceedings of the 3rd international conference on advanced data mining and applications, series ADMA ’07. Springer, Berlin/Heidelberg, pp 158–169. [Online]. Available http://dx.doi.org/10.1007/978-3-540-73871-8_16

Zhang Y, Meratnia N, Havinga P (2009a) Adaptive and online one-class support vector machine-based outlier detection techniques for wireless sensor networks. In: Proceedings of international conference on advanced information networking and applications workshops WAINA ’09, pp 990–995

Zhang Y, Meratnia N, Havinga PJM (2009b) Hyperellipsoidal svm-based outlier detection technique for geosensor networks. In: Third international conference on geosensor networks, Oxford, UK, series lecture notes in computer science, vol 5659. Springer, Berlin, July 2009, pp 31–41

Zhang Y, Meratnia N, Havinga P (2010) Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun Surv Tutor 12(2):159–170CrossRef

Zhang Y, Hamm NAS, Meratnia N, Stein A, van de Voort M, Havinga PJM (2012) Statistics-based outlier detection for wireless sensor networks. Int J Geogr Inf Sci 26(8):1373–1392

Zhuang Y, Chen L (2006) In-network outlier cleaning for data collection in sensor networks. In: In CleanDB, workshop in VLDB. APPENDIX 2006, pp 41–48

Zoumboulakis M, Roussos G (2007) Escalation: complex event detection in wireless sensor networks. Smart Sens Context 4793:270–285

Titel: Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey
verfasst von: Nauman Shahid
Ijaz Haider Naqvi
Saad Bin Qaisar
Publikationsdatum: 01.02.2015
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 2/2015
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-012-9370-y

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