2016 12th International Conference on Semantics, Knowledge and Grids (2016)
Aug. 15, 2016 to Aug. 17, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SKG.2016.017
In this study, we introduce a big data processing framework that provides self-healing capability in the Internet of Things domain. We discuss the high-level architecture of this framework and its prototype implementation. To identify faulty conditions, we utilize a complex-event processing technique by applying a rule-based pattern-detection algorithm on the events generated real-time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed architecture, we test the prototype implementation for performance and scalability under increasing incoming message rates. The results are promising, because its processing overhead is negligible.
complex event processing, big data, internet of things, self-healing systems, predictive maintenance
B. Dundar, M. Astekin and M. S. Aktas, "A Big Data Processing Framework for Self-Healing Internet of Things Applications," 2016 12th International Conference on Semantics, Knowledge and Grids(SKG), Beijing, China, 2016, pp. 62-68.