An efficient resource allocation scheme in a dense RFID network based on cellular learning automata

摘要

Radio-frequency identification (RFID) is a wireless communication technology. Radio frequencies can cause interference in a dense RFID system, thus decreasing efficiency. In recent years, many protocols have been proposed to reduce reader collisions based on multiple-access techniques. The main weakness of Time Division Multiple Access (TDMA)-based schemes is the random selection of resources. Additionally, they do not consider the distance between the interfering readers. Therefore, the likelihood of interference in an RFID system will be increased. To address this problem, we propose a new scheme for allocating resources to readers using a learning technique. The proposed scheme takes into account the distance between interfering readers, and these readers acquire the necessary knowledge to select new resources based on the results of the previous selection of neighboring readers using cellular learning automata. This approach leads to reduced interference in an RFID system. The proposed scheme is fully distributed and operates without hardware redundancy. In this scheme, the readers select new resources without exchanging information with each other. The simulation results show that the percentage of kicked readers decreased by more than 20%, and the proposed scheme also provides higher throughput than do state-of-the-art schemes for dense reader environments and leads to further recognition of tags.