Name
Abstract | ||
|---|---|---|
Wireless sensor networks (WSNs) are commonly used to monitor changes in an environment and prevent disasters such as structural instability, forest fires, and tsunami. WSNs should rapidly respond to changes, and must process and analyze sensor data in a distributed way to minimize battery consumption. On the other hand, machine learning (ML) algorithms are a powerful tool for data analyzing. However, ML algorithms are so complex that cannot be executed on resource constrained sensor nodes. Another challenge of using ML algorithms in WSNs is that ML algorithms are difficult to be distributed on every sensor node. Because ML algorithms are based on statistics' methods that need collecting amount of data to approach accuracy. In this paper, we propose a method that divides a logistical regression ML method into two steps, then distributes the two steps into sink nodes and sensor nodes to detect faulty sensor data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDCSW.2017.37 | 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW) |
Keywords | Field | DocType |
faulty sensor data detection,wireless sensor network,WSN,battery consumption minimization,machine learning algorithm,resource constrained sensor nodes,logistical regression ML method,sink nodes | Sensor node,Training set,Key distribution in wireless sensor networks,Data detection,Computer science,Prediction algorithms,Mobile wireless sensor network,Artificial neural network,Wireless sensor network,Distributed computing | Conference |
ISSN | ISBN | Citations |
1545-0678 | 978-1-5386-3293-2 | 0 |
PageRank | References | Authors |
0.34 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyu Zhang | 1 | 32 | 12.27 |
| Qian Zhao | 2 | 10 | 2.35 |
| Yukikazu Nakamoto | 3 | 79 | 21.50 |