Real-Time Implementation of Locality Sensitive Hashing Using NI WSN and LabVIEW for Outlier Detection in Wireless Sensor Networks

Outlier detection is one of the major challenges in wireless sensor networks. To the best of our knowledge, not many have evaluated the performance of outlier detection algorithms in real time. This paper proposes a real time hardware implementation of outlier detection using locality sensitive hashing algorithm for temperature data and evaluated both in indoor and outdoor environments. NI WSN 3202 programmable nodes and NI 9792 programmable gateway is used for hardware implementation of the algorithm and LabVIEWTM software is used for programming the nodes. The LSH technique detects outliers based on the threshold value. Hence the outlier detection accuracy and precision are analyzed for various threshold values and the optimal threshold value is selected.

[1]  Yang Xiao,et al.  Outlier detection based fault tolerant data aggregation for wireless sensor networks , 2011, 2011 5th International Conference on Application of Information and Communication Technologies (AICT).

[2]  Saba Mylvaganam,et al.  Intermediate measurement node for extension of WSN coverage , 2013, J. Cyber Secur. Mobil..

[3]  Jure Leskovec,et al.  Mining of Massive Datasets: Finding Similar Items , 2011 .

[4]  Hugo Martins,et al.  A support vector machine based technique for online detection of outliers in transient time series , 2015, 2015 10th Asian Control Conference (ASCC).

[5]  K. Srinathan,et al.  LSH based outlier detection and its application in distributed setting , 2011, CIKM '11.

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

[7]  Yaning Liu,et al.  Anomaly Detection in Medical Wireless Sensor Networks , 2013, J. Comput. Sci. Eng..

[8]  Osman Hegazy,et al.  Outliers detection and classification in wireless sensor networks , 2013 .

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

[10]  Laurent Amsaleg,et al.  Locality sensitive hashing: A comparison of hash function types and querying mechanisms , 2010, Pattern Recognit. Lett..

[11]  K. R. Krishnanand,et al.  Outlier detection and data filtering for wireless sensor and actuator networks in building environment , 2015, 2015 IEEE International Conference on Building Efficiency and Sustainable Technologies.

[12]  Jure Lescovek.  Finding Similar Items , 2012 .

[13]  Yannis Kotidis,et al.  Distributed similarity estimation using derived dimensions , 2011, The VLDB Journal.

[14]  Mahsa Salehi,et al.  Local outlier detection for data streams in sensor networks: Revisiting the utility problem invited paper , 2015, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[15]  Michael A. Casey,et al.  Locality-Sensitive Hashing for Finding Nearest Neighbors , 2008 .

[16]  Sanjay Kumar Madria,et al.  A Survey of Methods for Finding Outliers in Wireless Sensor Networks , 2013, Journal of Network and Systems Management.