SPQ: An Improved Q Algorithm Based on Slot Prediction

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Mitigating tag collisions is paramount for enhancing throughput in Radio Frequency Identification (RFID) systems. However, traditional algorithms encounter challenges like slot wastage and inefficient frame length adjustments. To tackle these challenges, the Slot Prediction Q (SPQ) algorithm was introduced, integrating the Vogt-II prediction algorithm and slot grouping to improve the initial Q value by predicting the first frame. This method quickly estimates the number of tags based on slot utilization, accelerating Q value adjustments when slot utilization is low. Furthermore, a Markov decision chain is used to optimize the relationship between the number of slot groupings (x) and the Q value. The Whale Optimization Algorithm (WOA) is applied to fine-tune the learning rate (C) and Q value in the traditional Q algorithm. Simulation results demonstrate that SPQ significantly reduces the total slots used during the reading process and improves RFID system throughput compared to traditional Q, FastQ, Subset Enhanced Performance-Q (SUBEP-Q), and Threshold Grouping Dynamic Q (TGDQ) algorithms. Specifically, compared to the traditional Q algorithm, SPQ increases the average Identification Speed by 7.20%, System Efficiency by 11.08%, and Time Efficiency by 5.69%.

This publication has 17 references indexed in Scilit: