Grouping based radio frequency identification anti-collision protocols for dense internet of things application

Abstract

Radio frequency identification (RFID) is an important internet of things (IoT) enabling technology. In RFIDs collision occur among tags because tags share communication channel. This is called tag collision problem. The problem becomes catastrophic when dense population of tags are deployed like in IoT. Hence, the need to enhance existing dynamic frame slotted ALOHA (DFSA) based electronic product code (EPC) C1G2 media access control (MAC) protocol. Firstly, this paper validates through simulation the DFSA theory that efficiency of the RFID system is maximum when the number tags is approximately equal to the frame size. Furthermore, literature review shows tag grouping is becoming popular to improving the efficiency of the RFID system. This paper analyzes selected grouping-based algorithms. Their underlining principles are discussed including their tag estimation methods. The algorithms were implemented in MATLAB while extensive Monte Carlo simulation was performed to evaluate their strengths and weaknesses. Results show that with higher tag density, fuzzy C-means based algorithm (FCMBG) outperformed traditional DFSA by over 40% in terms of throughput rate. The results also demonstrate FCMBG bettered other grouping-based algorithms (GB-DFSA and GBSA) whose tag estimation method are based on collision slots in terms slot efficiency by over 10% and also in terms of identification time.