Capturing Tag Dynamics by Prediction for Pervasive Internet-of-Things Applications

Efficient detection of RFID-tagged physical objects is one of the key enabling technologies to build pervasive Internet-of-Things applications. However, the detection of tagged-objects is faced with the critical challenge of tag dynamics, which mainly arises from the movement of tagged physical objects. To capture tag dynamics, the application needs to detect the presence/absence of tags in an accurate, timely and cost-effective way. To address these challenges, we propose the Prediction of Tag Dynamics (PTD) algorithm. PTD achieves runtime detection of tagged-objects by i) streaming of the temporally-correlated tag readings obtained from persistent tracking of the tagged-object, and ii) runtime prediction of tag dynamics based on the streaming of tag readings. The performance of PTD is investigated based on real implementation and experimental evaluation, where PTD processes tag readings gathered with high fidelity from persistent tracking of real activities of tagged objects. The evaluation results demonstrate the accuracy, timeliness and cost-effectiveness of PTD.