Estimation of RFID Tag Population Size by Gaussian Estimator

Radio Frequency IDenti cation (RFID) systems are prevalent in all sorts of daily life endeavors. In this thesis we propose a new method to estimate RFID tag population size. We have named our algorithm Gaussian Estimation of RFID Tags, namely, GERT. We present GERT under both {0,1} and {0,1,e} channel models, and in both cases the estimator we use is a well justi ed Gaussian random variable for large enough frame size based on Central Limit Theorem for triangular arrays. The most prominent feature of GERT is the quality with which it estimates a tag population size. We support all the required approximations with detailed analytical work and account for all the approximation errors when we consider the overall quality of the estimation. Our simulation results agree well with analytical ones. GERT, based on standardized frame slotted Aloha protocol, can estimate any tag population size with desired level of accuracy using fewer number of frame slots than previously proposed algorithms

Estimation of RFID tag population size by gaussian estimator

Radio Frequency IDentification (RFID) systems are prevalent in all sorts of daily life endeavors. Most previous tag estimation schemes worked with relatively smaller frame size and large number of rounds. Here we propose a new estimator named \textquotedblleft Gaussian Estimator of RFID Tags,\textquotedblright (GERT), that works with large enough frame size to be accurately approximated to Gaussian distribution within a frame. The selection of the frame size is done according to Triangular Array Central Limit Theorem which also enables us to quantify the approximation error. Larger frame size helps the statistical average to converge faster to the ensemble mean of the estimator and the quantification of the approximation error helps to determine the number of rounds to keep up with the accuracy requirements. The overall performance of GERT is better than the previously proposed schemes considering the number of slots required for estimation to achieve a given level of estimation accuracy