Abstract
Many trace-based diagnostic techniques have been proposed for abnormal detection and fault diagnosis in networked embedded systems such as wireless sensor networks (WSNs). Event tracing is a nontrivial task for resource-constrained embedded devices. Existing tracing approaches employ compression algorithms to reduce the trace size. However, these approaches either are inapplicable or perform poorly. In this paper, we propose TOC, a novel event tracing technique using online compression. TOC combines periodical pattern mining and efficient token assignment, effectively reducing the trace size with acceptable execution overhead. We implement TOC based on TinyOS 2.1.2 and evaluate its effectiveness by case studies in sensor network applications. Results show that TOC reduces the trace size by 52.2%, compared with LIS—a state-of-the-art event tracing method.
Highlights
The deep integration of embedded systems and networked systems has promoted the rapid development of networked embedded systems such as wireless sensor networks (WSNs)
We exploit the fact that the behaviors of WSN application are highly repetitive and do not evolve much over time to propose a novel online trace compression technique, called TOC
TOC can adapt to various WSN applications online based on their system behaviors
Summary
The deep integration of embedded systems and networked systems has promoted the rapid development of networked embedded systems such as wireless sensor networks (WSNs). These systems have been successfully applied in various scientific as well as industrial domains to support numerous applications such as environment monitoring [1], structural protection [2], and ecosystem management [3]. WSNs are highly susceptible to deployment failures as they are deployed in austere environments such as volcanoes or mountains. An effective way to troubleshoot the root cause of failures is to trace important system events
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