Name
Abstract | ||
|---|---|---|
One of the issues faced by manufacturing industry is a lack of automatic localization techniques. In this research, a Radio Frequency Identification (RFID) based localization system is proposed for resource tracking. In this study, we incorporated a RFID tag at each item to be tracked, and a RFID reference tag at each location zone. The encoded IDs are read to identify the names of items and location zones. At the same time, radio signals (received signal strength and phase) are measured as RFID fingerprints. Similarity measures are studied to compare fingerprints between RFID item tags and location reference tags to track the location of the items. The kernel-based learning method was implemented as similarity measure. Different cluster labelling methods were compared and it was found that the proximity method is more efficient. The clustering method is used to overcome the issues faced by traditional RFID based localization methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICARCV.2018.8581143 | 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) |
Field | DocType | ISSN |
Kernel (linear algebra),Data mining,Similarity measure,Computer science,Control engineering,Signal strength,Localization system,Cluster analysis,Radio-frequency identification | Conference | 2474-2953 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng Huang | 1 | 56 | 13.27 |
| Syed Shoaib | 2 | 0 | 0.34 |
| Andri Ashfahani | 3 | 5 | 2.10 |
| Mahardhika Pratama | 4 | 702 | 50.02 |