Abstract
Radio Frequency Identification (RFID) technology has become an important tool for items identification and tracking. Its rapid deployment in dynamic industry areas enables variety of new applications in behavioural analysis and processes automation. In such systems it is crucial to identify all RFID tags before they leave the interrogation area. To accomplish fast identification, it is necessary to estimate a number of tags which are in the reader interrogation area. In this paper we revisit the problem of tag quantity estimate and adapting frame size of Dynamic Frame Slotted ALOHA (DFSA) widely used as RFID Medium Access Control (MAC) mechanism. The main disadvantage in the implementation of the state-of-art estimation algorithms includes the number of required computations, along with the temporary storage of large numbers which appear before estimation. As a consequence, such algorithms are energy inefficient and may require specific computer architecture to support the calculus. In order to address stated issues, we present Improved Linearized Combinatorial Model (ILCM) algorithm for optimal frame size selection which, due to linear property of the estimator, allows significant reduction in the estimate computation with acceptable trade-off in accuracy. Simulations analyse the required number of slots to identify tag population and the Floating Point Operation (FLOP) costs required to compute estimate. In addition, to emphasize the importance of reducing computational cost, we give a case study which compares energy consumed by the mobile RFID reader processor to compute the estimate and its energy equivalent of the required radio front-end energy to identify tags.
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