With the increase in the number of tags, an efficient approach of tag identification is becoming an urgent need in Industrial Internet of Things (IIoT). However, the identification performance of existing Aloha-based anticollision schemes is limited when the initial frame size is seriously mismatched with the actual tag population size. The performance will degrade further when IIoT is deployed in the error-prone channel environment. To optimize the identification performance of RFID system in an error-prone channel environment, we propose an efficient early frame breaking strategy based anticollision algorithm (EFB-ACA) with channel awareness. The EFB-ACA divides the whole tag identification process into two phases: convergence phase and identification phase. The function of convergence phase is to make the adjusted frame quickly converge to an appropriate size. The early frame breaking strategy is embedded in the convergence phase. Numerical results show that the proposed EFB-ACA algorithm outperforms the other methods on efficiency and stability in the error-prone channel. In addition, EFB-ACA algorithm also outperforms the other methods in the error-free channel.
RFID can automatically read the information stored on the tag. When multiple tags send data to the reader and writer at the same time, multiple tag collision will occur, resulting in the reader and writer being unable to successfully identify the tag. To solve this problem, an RFID multitag identification method based on decision tree is proposed. The Manchester code is used to represent different level states of voltage. The reader extracts the collision bits in the tag ID by using the Manchester code, and these collision bits form the new tag ID. In the process of decision tree search, the “XOR” command operation is added, and the query prefix is selectively sent to the tag to mark all collision bits. The process will continue until one tag is identified; then, reset the remaining unrecognized tags and continue to participate in a new round of identification. The simulation results show that the RFID multitag identification method based on decision tree can reduce the time complexity and communication overhead and improve the throughput, so it has high search efficiency.
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