"TagTone: Scalable RFID Communication through Multi-Frequency Analysis"
T2017-459 A novel solution for RFID communication.
RFID tags are utilized to track and monitor objects in shopping malls, warehouses, and inventories. These applications require large-scale dense deployments of RFID tags. In such scenarios, message collisions are unavoidable. Decoding these colliding messages from the tags is challenging due to hardware heterogeneity of tags and small message size. To decode colliding messages from a large number of tags, existing protocols require either highly accurate clocks (higher power consumption) or time synchronization across the tags. Thus, there is a need to decode these collided messages in an efficient manner.
Researchers at The Ohio State University, led by Tanmoy Das, propose a new scalable protocol that can decode a large number of colliding tags by exploiting the large bandwidth (in order of tens of MHz). The proposed protocol leverages the independence of channels and messages from different tags to decode colliding messages. This solution can decode collided messages from a large number of RFID tags even when the transmitted messages are affected by the hardware heterogeneity of the tags. Decoding is formulated as a statistical signal processing problem which is effectively solved by a technique called Independent Component Analysis (ICA).
- RFID Communication
- FFT based filtering method allows for collections of samples at multiple frequencies simultaneously
- Improved efficiency and performance of ICA on received samples
- Decodes 2 times more tags than existing protocols experiments
- Does not require any information regarding the channels