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An efficient resource allocation scheme in a dense RFID network based on cellular learning automata
Author(s) -
Assarian Ali,
Khademzadeh Ahmad,
Hosseinzadeh Mehdi,
Setayeshi Saeed
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3835
Subject(s) - computer science , redundancy (engineering) , scheme (mathematics) , time division multiple access , learning automata , computer network , interference (communication) , radio frequency identification , throughput , wireless , resource allocation , distributed computing , automaton , artificial intelligence , telecommunications , computer security , mathematical analysis , channel (broadcasting) , mathematics , operating system
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.