Dynamic tag estimation for optimizing tree slotted aloha in RFID networks

The emergent commercial use of techniques for Radio Frequency-based IDentification of different items (RFID) requires the investigation and testing of collision resolution mechanisms for the efficient and correct communication between the system reader and the tags labeling the items that need to be identified. Several MAC protocols have been proposed to resolve collisions in RFID networks. A recent solution, named Tree Slotted Aloha (TSA), has been shown to outperform previous ones with respect to the time it takes for identifying all tags, and the total number of bits transmitted to complete the identification process. However, almost half of the time needed by TSA for identifying tags is spent in collisions. This depends on TSA operation and in particular on the way TSA estimates the number of colliding tags. We have observed that in the case of realistically large networks, TSA highly underestimates this number, with non-negligible impact on the protocol performance. In this paper, we propose a Dynamic Tree Slotted Aloha (Dy TSA) protocol that exploits the knowledge acquired during ongoing readings to refine the estimation of the number of colliding tags. In so doing, Dy TSA adapts the length of the following reading cycles to the actual number of tags still requiring identification. Through ns2-based simulations we show that the proposed method is effective for tag identification and results in significantly improved performance over TSA. Specifically, the length of the identification process is up to 20% lower than that of TSA. Furthermore, the amount of transmitted bits needed for identifying all tags decreases up to 30%.