Performance of outlier detection techniques based classification in Wireless sensor networks

Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This paper aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

[1]  Marimuthu Palaniswami,et al.  Centered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor Networks , 2010, IEEE Transactions on Information Forensics and Security.

[2]  Marimuthu Palaniswami,et al.  Detecting data anomalies in wireless sensor networks , 2010 .

[3]  Mohamed Abid,et al.  Fast and Efficient Outlier Detection Method in Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[4]  Nirvana Meratnia,et al.  Outlier Detection Techniques for Wireless Sensor Networks: A Survey , 2008, IEEE Communications Surveys & Tutorials.

[5]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003 .

[6]  Ian F. Akyildiz,et al.  A survey on wireless multimedia sensor networks , 2007, Comput. Networks.

[7]  Biming Tian,et al.  Anomaly detection in wireless sensor networks: A survey , 2011, J. Netw. Comput. Appl..

[8]  Haixia Xu,et al.  Adaptive kernel principal component analysis , 2010, Signal Process..

[9]  Marimuthu Palaniswami,et al.  Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks , 2007, 2007 IEEE International Conference on Communications.

[10]  Giuseppe Lo Re,et al.  Adaptive Distributed Outlier Detection for WSNs , 2015, IEEE Transactions on Cybernetics.

[11]  Helena Rifà-Pous,et al.  A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks , 2016, Sensors.

[12]  Marimuthu Palaniswami,et al.  CESVM: Centered Hyperellipsoidal Support Vector Machine Based Anomaly Detection , 2008, 2008 IEEE International Conference on Communications.

[13]  Saad B. Qaisar,et al.  Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey , 2012, Artificial Intelligence Review.

[14]  Nirvana Meratnia,et al.  Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine , 2013, Ad Hoc Networks.