Name
Abstract | ||
|---|---|---|
This paper investigates the precise identification of physical phenomena in the Internet of Things (IoT) context, which is one of the main challenges when dealing with the massive scale of IoT data. For this, we use information theory quantifiers in the characterization and classification of physical phenomena to minimize the effects of the lack of proper descriptions and the high heterogeneity of IoT sensors. Thus, by understanding the dynamics behind physical phenomena, we perform the classification of sensor data based on their expected behavior, not their data points. By using a simple classification algorithm, we show that the behavioral dynamics of some physical phenomena are more affected by different geographical regions than others. This gives a classification accuracy of 75% when all phenomena are considered and of 93% when considering only the invariant ones, with a worst case of false positives of 12%. This result indicates the high potential of our technique to correctly identify physical phenomena from sensor data, a fundamental issue for several applications, even in an unreliable IoT environment. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DCOSS.2019.00125 | 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) |
Keywords | DocType | ISSN |
Internet of things,Time series characterization,Time series classification,Information theory quantifiers | Conference | 2325-2936 |
ISBN | Citations | PageRank |
978-1-7281-0571-0 | 0 | 0.34 |
References | Authors | |
14 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João B. Borges Neto | 1 | 0 | 0.68 |
| Heitor Ramos | 2 | 196 | 18.09 |
| Raquel A. F. Mini | 3 | 248 | 23.58 |
| Aline Carneiro Viana | 4 | 497 | 37.11 |
| Antonio Loureiro | 5 | 2406 | 197.77 |