Name
Abstract | ||
|---|---|---|
In many applications of Internet of Things (IoT), the huge amount of data are generated by sensor nodes and processing them are complex. Offloading data classification and anomaly event detection tasks to sink nodes in sensor networks can reduce the computing complexity, lower remote communication loads, and improve the response time for the delay-sensitive IoT applications. Many existing classifi... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JIOT.2018.2884485 | IEEE Internet of Things Journal |
Keywords | Field | DocType |
Internet of Things,Neural networks,Event detection,Learning systems,Training,Anomaly detection,Data models | Anomaly detection,Data modeling,Computer science,Response time,Hybrid neural network,Data classification,Artificial neural network,Energy consumption,Wireless sensor network,Distributed computing | Journal |
Volume | Issue | ISSN |
6 | 2 | 2327-4662 |
Citations | PageRank | References |
15 | 0.58 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dapeng Wu | 1 | 4463 | 325.77 |
| Hang Shi | 2 | 15 | 0.58 |
| Honggang Wang | 3 | 1365 | 124.06 |
| Ruyan Wang | 4 | 241 | 40.80 |
| Hua Fang | 5 | 343 | 32.48 |