Blind anti-collision methods for RFID system: a comparative analysis

Abstract

Radio Frequency Identification (RFID) is one of the critical technologies of the Internet of Things (IoT). With the rapid development of IoT and the extensive use of RFID in our life, the step of RFID development should be faster. However, the tags in an RFID system are more and more utilized, both of them communicate in the same channel. The signal the reader received is mixed, and the reader cannot get the correct message the tags send directly. This phenomenon is often called a collision, which is the main obstacle to the development of the RFID system. Traditionally, the algorithm to solve the collision problem is called the anti-collision algorithm, the widely used anti-collision algorithm is based on Time Division Multiple Access (TDMA) like ALOHA-based and Binary search-based anti-collision algorithm. The principle of the TDMA-based anti-collision algorithm is to narrow the response of tags to one in each query time. These avoidance anti-collision algorithms performance poor when the number of tags is huge, thus, some researchers proposed the Blind Source Separation (BSS)-based anti-collision algorithm. The blind anti-collision algorithms perform better than the TDMA-based algorithms; it is meaningful to do some more research about this filed. This paper uses several BSS algorithms like FastICA, PowerICA, ICA_p, and SNR_MAX to separate the mixed signals in the RFID system and compare the performance of them. Simulation results and analysis demonstrate that the ICA_p algorithm has the best comprehensive performance among the mentioned algorithms. The FastICA algorithm is very unstable, and has a lower separation success rate, and the SNR_MAX algorithm has the worst performance among the algorithms applied in the RFID system. Some advice for future work will be put up in the end.