Name
Abstract | ||
|---|---|---|
Wireless connectivity plays a crucial role in collecting data from a large number of devices and sensors for various Internet-of-Things (IoT) applications including supply chain management and personalized healthcare. In most IoT applications, for various reasons, a collected data set may include incorrect or corrupted data samples, which should be detected and removed. For example, malicious devices may send fake information or malfunctioned remote devices can respond improperly. In this paper, we study anomaly detection for wireless links, not data sets sent by devices, to see any anomalies in the physical and link layers associated with connected devices to a network. The resulting approach can be viewed as preemptive anomaly detection and he part of causal anomaly discovery that helps determine whether anomalies detected in a data set are caused by errors in wireless links or transceivers. |
Year | Venue | DocType |
|---|---|---|
2021 | 2021 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC) | Conference |
ISSN | Citations | PageRank |
2309-9402 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nemati Mahyar | 1 | 6 | 1.87 |
| Jihong Park | 2 | 0 | 0.34 |
| Moongu Jeon | 3 | 456 | 72.81 |
| Jinho Choi | 4 | 1642 | 206.06 |