Name
Abstract | ||
|---|---|---|
RFID sensor networks perpetually stream sensor data without batteries. Cameras are power hungry but provide richer information than conventional sensor network nodes. Battery-free, RF-powered camera sensor nodes combine many of the attractive features of RFID sensor networks with those of cameras. However, prior battery-free cameras have no notion of 3D location, which is desirable for creating large scale networks of battery free cameras. In this work we propose using battery-free RFID sensor tags enhanced with on-board cameras to enable a network of distributed tags to optically determine the 3D location and pose of each camera tag given known reference tags enhanced with LEDs. Experimental results show that the camera tags are capable of determining their position with an average accuracy of [x, y, z] = [15:92cm, 4:39cm, 1:03cm] at an LEDs-to-Camera range within 3.6m. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2750858.2805846 | ACM International Conference on Ubiquitous Computing |
Keywords | Field | DocType |
RFID, Camera, Self-Localization, Sensor Networks, Battery-Free | Computer vision,Key distribution in wireless sensor networks,Self localization,Image sensor,Computer science,Visual sensor network,Real-time computing,Human–computer interaction,Artificial intelligence,Battery (electricity),Wireless sensor network | Conference |
Citations | PageRank | References |
9 | 0.69 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| saman naderiparizi | 1 | 112 | 6.75 |
| Yi Zhao | 2 | 40 | 4.57 |
| James Youngquist | 3 | 9 | 0.69 |
| Alanson P Sample | 4 | 186 | 16.26 |
| Smith Joshua R. | 5 | 2027 | 361.75 |