PROJECT TITLE :

Capture-Aware Estimation for Large-Scale RFID Tags Identification

ABSTRACT:

The way to estimate the number of passive radio frequency identification (RFID) tags and also the prevalence chance of capture result is very important for a dynamic frame length Aloha RFID system with capture effect. The estimation would relate to setting an optimal frame length, that makes tag identification achieve higher efficiency. Under massive-scale tags identification atmosphere, the quantity of tags could be a lot of larger than an initial frame length. In this situation, existing estimates don't work well. In this letter, we have a tendency to propose a unique estimation methodology for the big-scale tags identification. The proposed method may modify the initial frame length matched to the quantity of tags from solely the first many slots within the frame. The advantage of the proposed methodology is to figure higher even when the amount of tags is a lot of larger. Numerical results show that, the proposed methodology has lower estimation errors below the big-scale tag identification. When setting an optimal frame length from the estimated results of the proposed technique, furthermore, we tend to might acquire higher identification efficiency.