Joined Q-ary Tree Anti-Collision for Massive Tag Movement Distribution

Abstract

Radio-Frequency Identification (RFID) systems consist of tags and networked electromagnetic readers. Despite the emergence of RFID technology, the problem of identifying multiple tags, due to the Collisions is still a major problem. The problem can be solved by using anti-collision methods such as \emph{ALOHA-based} approaches and Tree-based approaches. ALOHA-based approaches suffer from tag starvation, which causes that not all tags can be identified. The tree-based approaches suffer from too long identification delay caused by lengthy queries during identification process. In this paper, we propose a tree-based anti-collision method called ``Joined Q-ary Tree'', which adaptively adjusts tree branches according to tag movement behavior and number of tags within an interrogation zone. In this empirical study, we demonstrate that the proposed method is suitable for numerous scenarios. It requires less queries issued per complete identification than existing approaches while ensuring identification of all tags within the interrogation zone.