Title :
Bugs or anomalies? Sequence mining based debugging in wireless sensor networks
Author :
Kefa Lu ; Qing Cao ; Thomason, M.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN, USA
Abstract :
WSN applications are prone to bugs and failures due to their typical characteristics, such as being extensively distributed, heavily concurrent, and resource restricted. In this paper, we propose and develop a flexible and iterative WSN debugging system based on sequence mining techniques. At first, we develop a data structure called the vectorized Probabilistic Suffix Tree (vPST), an elastic model to extract and store sequential information from program runtime traces in compact suffix tree based vectors. Then, we build a novel WSN debugging system by integrating vPST with Support Vector Machines (SVM), a robust and generic classifier for both linear and nonlinear data classification tasks. Finally, we demonstrate that the vPST-SVM debugging system is efficient, flexible, and generic by three different test cases, two on the LiteOS operating system and one on the TinyOS operating system.
Keywords :
data mining; data structures; iterative methods; operating systems (computers); pattern classification; probability; program debugging; support vector machines; telecommunication computing; trees (mathematics); vectors; wireless sensor networks; LiteOS operating system; TinyOS operating system; compact suffix tree based vectors; data structure; elastic model; generic classifier; iterative WSN debugging system; nonlinear data classification tasks; program runtime traces; robust classifier; sequence mining based debugging; sequence mining techniques; sequential information; support vector machines; vPST-SVM debugging system; vectorized probabilistic suffix tree; wireless sensor networks;
Conference_Titel :
Mobile Adhoc and Sensor Systems (MASS), 2012 IEEE 9th International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4673-2433-5
DOI :
10.1109/MASS.2012.6502549