An efficient resource allocation scheme in a dense RFID network based on cellular learning automata. (13th November 2018)

• Record Type: Journal Article
• Authors: Assarian, Ali; Khademzadeh, Ahmad; Hosseinzadeh, Mehdi; Setayeshi, Saeed
• Is Part Of: International journal of communication systems. Volume 32:Number 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-11-13
• Subjects: cellular learning automata -- dense RFID system -- interference -- reader collision; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.3835
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: 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. Abstract : In this paper, we propose a new scheme for allocating resources to readers in dense RFID system using a learning technique. The proposed scheme taking into account the distance between interfering readers and readers acquires the necessary knowledge to select new resources based on the results of the previous selection of neighboring readers using cellular learning automata. The major contribution of this manuscript is: In the proposed scheme, resources are selected consciously and not randomly in a dense RFID system. Readers acquire the required knowledge to select new resources to create less interference using a learning model. In the proposed scheme, the distance between readers as a contributing factor in interference is considered We conducted a series of experiments to evaluate the performance of proposed approach for different metrics.