Name
Abstract | ||
|---|---|---|
Wireless Sensor Networks (WSNs) are a powerful instrument for monitoring and recording physical phenomena. Very often the quality of the sensed data collected by sensor nodes is affected by noise and errors, events, and malicious attacks. Also, the processing and the transmitting of this data over the network may drain the amount of available resources of WSNs and decrease rapidly the network lifetime. Therefore, there is an urgent need to detect faulty data in order to insure the reliability of data and keep the resource of WSNs at a high level. In this paper, we propose a new approach for faulty data detection in WSNs based on Copula theory. Our experimental results on real data sets collected by real sensor networks show that a significant percentage of the data are faulty. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/3010089.3010114 | Proceedings of the International Conference on Big Data and Advanced Wireless Technologies |
Field | DocType | ISBN |
Key distribution in wireless sensor networks,Data mining,Data set,Data detection,Computer science,Copula (linguistics),Brooks–Iyengar algorithm,Outlier,Real-time computing,Wireless sensor network,Copula theory | Conference | 978-1-4503-4779-2 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farid Lalem | 1 | 21 | 4.54 |
| Ahcène Bounceur | 2 | 306 | 35.05 |
| Rahim Kacimi | 3 | 77 | 11.44 |
| Reinhardt Euler | 4 | 95 | 28.50 |
| Massinissa Saoudi | 5 | 8 | 1.87 |