Bugs or anomalies? Sequence mining based debugging in wireless sensor networks

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.