Access the full text.
Sign up today, get DeepDyve free for 14 days.
R Cardell-Olivera, M Kranza, K Smettemb, K Mayerc (2005)
A reactive soil moisture sensor network: design and field evaluationInt J Distrib Sens Netw, 1
IA Dario, IF Akyildiz, D Pompili, T Melodia (2005)
Underwater acoustic sensor networks: research challengesAd Hoc Netw Elsevier, 3
B Scholkopf, AJ Smola (2001)
Learning with kernels: support vector machines, regularization, optimization, and beyond
S Rajasegarar, C Leckie, M Palaniswami (2008)
Anomaly detection in wireless sensor networksIEEE Wirel Commun, 15
P Hao, J Chiang, Y Lin (2009)
A new maximal-margin spherical-structured multi-class support vector machineAppl Intell, 30
D Tax, R Duin (2004)
Support vector data descriptionMach Learn, 54
S Boyd, L Vandenberghe (2004)
Convex optimization
V Barnett, T Lewis (1994)
Outliers in statistical data
A Sharma, L Golubchik, R Govindan (2010)
Sensor faults: detection methods and prevalence in real-world datasetsACM Trans Sens Netw (TOSN), 6
D Yeung, D Wang, W Ng, E Tsang, X Wang (2007)
Structured large margin machines: sensitive to data distributionsMach learn, 68
E Bredensteiner, K Bennett (1999)
Multicategory classification by support vector machinesComput Optim Appl, 12
S Ma, J Wang, Z Liu, H Jiang (2013)
Density-based distributed elliptical anomaly detection in wireless sensor networksAppl Mech Mater, 249
A Navia-Vazquez, D Gutierrez-Gonzalez, E Parrado-Hernandez, J Navarro-Abellan (2006)
Distributed support vector machinesIEEE Trans Neural Netw, 17
CF García-Hernández, PH Ibargüengoytia-González, J García-Hernández, JA Pérez-Díaz (2007)
Wireless sensor networks and applications: a surveyIJCSNS Int J Comput Sci Netw Secur, 7
S Cheong, S Oh, S Lee (2004)
Support vector machines with binary tree architecture for multi-class classificationNeural Inf Process Lett Rev, 2
A Elisseeff, J Weston (2002)
Kernel methods for multi-labelled classification and categorical regression problemsAdv Neural Inf Process Syst, 14
C Hsu, C Lin (2002)
A comparison of methods for multiclass support vector machinesIEEE Trans Neural Netw, 13
Y Zhang, N Meratnia, P Havinga (2010)
Outlier detection techniques for wireless sensor networks: a surveyIEEE Commun Surv Tutor, 12
V Gomez-Verdejo, J Arenas-Garcia, M Lazaro-Gredilla, A Navia-Vazquez (2011)
Adaptive one-class support vector machineIEEE Trans Signal Process, 59
P Misra, S Kanhere, D Ostry, S Jha (2010)
Safety assurance and rescue communication systems in high-stress environments: a mining case studyIEEE Commun Mag, 48
S Rajasegarar, C Leckie, J Bezdek, M Palaniswami (2010)
Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networksIEEE Trans Inf Forensics Secur, 5
AR Ganguly (2008)
Knowledge discovery from sensor data
IF Akyildiz, B Akan Özgür (2003)
Interplanetary internet: state-of-the-art and research challengesComput Netw, 43
E Dereszynski, T Dietterich (2011)
Spatiotemporal models for data-anomaly detection in dynamic environmental monitoring campaignsACM Trans Sens Netw (TOSN), 8
D Wang, DS Yeung, ECC Tsang (2006)
Structured one-class classificationIEEE Trans Syst Man Cybern Part B Cybern, 36
I Steinwart, A Christmann (2008)
Support vector machines (information science & statistics)Recherche, 67
W Wu, X Cheng, M Ding, K Xing, F Liu, P Deng (2007)
Localized outlying and boundary data detection in sensor networksIEEE Trans Knowl Data Eng, 19
S George, W Zhou, H Chenji, M Won, Y Lee, A Pazarloglou, R Stoleru, P Barooah (2010)
Distressnet: a wireless ad hoc and sensor network architecture for situation management in disaster responseIEEE Commun Mag, 48
R Herbrich (2002)
Learning kernel classifiers: theory and algorithms
Z Liu (2011)
A method of svm with normalization in intrusion detectionProcedia Environ Sci, 11
X Luo, M Dong, Y Huang (2006)
On distributed fault-tolerant detection in wireless sensor networksIEEE Trans Comput, 55
B Krishnamachari, S Iyengar (2004)
Distributed bayesian algorithms for fault-tolerant event region detection in wireless sensor networksIEEE Trans Comput, 53
IF Akyildiz, W Su, Y Sankarasubramaniam, E Cayirci (2002)
Wireless sensor networks: a surveyComput Netw, 38
M Bahrepour, N Meratnia, PJM Havinga (2010)
Fast and accurate residential fire detection using wireless sensor networksEnviron Eng Manag J, 9
J Weston, C Watkins (1999)
Support vector machines for multi-class pattern recognitionProc seventh Eur Symp Artif Neural Netw, 4
J Bezdek, S Rajasegarar, M Moshtaghi, C Leckie, M Palaniswami, T Havens (2011)
Anomaly detection in environmental monitoring networks [application notes]IEEE Comput Intell Mag, 6
V Bhuse, A Gupta (2006)
Anomaly intrusion detection in wireless sensor networksJ High Speed Netw, 15
M Moshtaghi, T Havens, J Bezdek, L Park, C Leckie, S Rajasegarar, J Keller, M Palaniswami (2011)
Clustering ellipses for anomaly detectionPattern Recognit, 44
Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.
Artificial Intelligence Review – Springer Journals
Published: Jan 29, 2013
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.