Radio Frequency Identication (RFID) is a technology that allows automatic identication of people or objects by incorporating the use of radio frequency waves to transmit data between networked electromagnetic readers and tags. RFID is considered an emerging technology for advancing a wide range of applications, such as supply chain management and distribution. However, despite the extensive development of the RFID technology in many areas, the RFID tags collision problems remain a serious issue. Collision problems occur due to the simultaneous presence of multiple numbers of tags within the reader zone. To solve collision problems, dierent anti-collision methods have been mentioned in literature. These methods are either insucient or too complex, with a high overhead cost of implementation. In this work, in order to improve the quality of RFID data collection, we propose novel deterministic and probabilistic anti-collision approaches. The main contributions of this study are summarised as follows: 1. We propose two novel deterministic anti-collision algorithms using combinations of Q-ary trees (Pupunwiwat and Stantic, 2009a,b, 2010c), with the intended goal to minimise memory usage queried by the RFID reader. By reducing the size of queries, the RFID reader can preserve memories, and the identication time can be improved. 2. We propose a novel frame-size estimation technique (Pupunwiwat and Stantic, 2010a,b) to minimise the number of slots and frames queried by the RFID reader and to maximise the system eciency. In addition, we introduce the probabilistic group-based anti-collision method (Pupunwiwat and Stantic, 2010d) to improve the overall performance of the tag recognition process. 3. We evaluate our proposed anti-collision techniques and perform a comparative anal- ysis, in order to nd the benets and disadvantages of each method. Additionally, in order to identify the best selection of anti-collision method, we propose two strate- gies for selective anti-collision technique management, i.e. a Novel Decision Tree Strategy and a Six Thinking Hats Strategy (Pupunwiwat et al., 2011). By correctly identifying the most suitable anti-collision method for specic scenarios, the quality of data collection can be improved.
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