Abstract
Monitoring and analyzing sensor networks is essential for exploring energy consumption in smart buildings or cities. However, the data generated by sensors are affected by various types of anomalies and this makes the analysis tasks more complex. Anomaly detection has been used to find anomalous observations from data. In this paper, we propose a Pattern-based method, for anomaly detection in sensor networks, entitled CoRP “Composition of Remarkable Point” to simultaneously detect different types of anomalies. Our method detects remarkable points in time series based on patterns. Then, it detects anomalies through pattern compositions. We compare our approach to the methods of literature and evaluate them through a series of experiments based on real data and data from a benchmark.
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Acknowledgment
This PhD thesis is financed by the Management and Exploitation Service (SGE) of Rangueil campus attached to the Rectorate of Toulouse and the research is made in the context of neOCampus project (Paul Sabatier University, Toulouse). The authors thank the SGE for providing access to actual sensor data. They also thank the experts who helped to understand this data and identify the anomalies observed during the operation.
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Ben Kraiem, I., Ghozzi, F., Peninou, A., Teste, O. (2020). CoRP: A Pattern-Based Anomaly Detection in Time-Series. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2019. Lecture Notes in Business Information Processing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-40783-4_20
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DOI: https://doi.org/10.1007/978-3-030-40783-4_20
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