A Machine Learning Auxiliary Approach for the Distributed Dense RFID Readers Arrangement Algorithm

Abstract

This paper is an extended version of the work published. Radio-frequency identification (RFID) is widespread in industries such as supply-chain management and logistics due to its low-cost feature. In many real-world problems, one often needs to leverage a considerable amount of RFID readers to cover a large area. Many graph-based dense RFID readers system anti-collision algorithms were proposed to address the collision problems. However, state-of-the-art collision avoidance algorithms are centralized algorithms. In a dense RFID system, the graphs generated by the centralized algorithms could be very complicated. Therefore, a centralized algorithm increases the computational workload of the central server. We proposed a distributed anti-collision algorithm based on the idea of a centralized collision avoidance algorithm called MWISBAII. In our later research, we found that due to the lack of global information, there is a gap between the performance of our distributed algorithm and the centralized MWISBAII. To narrow this gap, we introduced machine learning into the proposed algorithm. The machine learning model is an empirical model that mitigates the deficiency of the lack of global information. The experimental results show that the proposed distributed algorithm with machine learning can get almost the same performance as the centralized MWISBAII in different experimental settings. INDEX TERMS Large scale RFID network optimization, reader coverage collision avoidance (RCCA), maximum weight independent set (MWIS), machine learning (ML).

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