Name
Abstract | ||
|---|---|---|
Smart sensors, such as smart meters or smart phones, are nowadays ubiquitous. To be “smart”, however, they need to process their input data with limited storage and computational resources. In this paper, we convert the stream of sensor data into a stream of symbols, and further, to higher level symbols in such a way that common analytical tasks such as anomaly detection, forecasting or state recognition, can still be carried out on the transformed data with almost no loss of accuracy, and using far fewer resources. We identify states of a monitored system and convert them into symbols (thus, reducing data size), while keeping “interesting” events, such as anomalies or transition between states, as it is. Our algorithm is able to find states of various length in an online and unsupervised way, which is crucial since behavior of the system is not known beforehand. We show the effectiveness of our approach using real-world datasets and various application scenarios. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/PERCOM.2015.7146506 | Pervasive Computing and Communications |
Keywords | Field | DocType |
intelligent sensors,smart phones,anomaly detection,forecasting,higher level symbol,online unsupervised state recognition,sensor data,smart meter,smart phone,smart sensor,state recognition | Online learning,Data mining,Anomaly detection,State recognition,Computer science,Internet of Things,Supervised learning,Cluster analysis | Conference |
ISSN | Citations | PageRank |
2474-2503 | 1 | 0.37 |
References | Authors | |
20 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julien Eberle | 1 | 112 | 7.81 |
| Tri Kurniawan Wijaya | 2 | 140 | 14.20 |
| Karl Aberer | 3 | 6459 | 662.26 |