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
