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

Abstract

<span lang="EN-US">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.</span>

Efficient Tag Grouping RFID Anti-Collision Algorithm for Internet of Things Applications Based on Improved K-Means Clustering

“…Applying RFID to IoT demands dense deployment of tags. Hence, an accurate tag number estimation algorithm is imperative [6], [17]. In the existing tag number estimation algorithm, the RFID reader is not equipped with prior information of tag number before the actual tag identification but relies on broadcasting queries and checking the value of slots.…”

“…Reference [4] developed their tag estimate using results from sub-frames after query cycles, while [6] did a performance evaluation of a few grouping based RFID anticollision algorithms. Whereas [6] only analysed and evaluated existing grouping based algorithms without proffering a different method, [4] gave a tag estimate after query cycles. However, this paper develops a new algorithm that estimates tag number priori the actual tag reading.…”

“…Passive UHF RFID (unlike active and semi-passive RFIDs), because their tags do not require batteries, are very cheap to be deployed on a billion scale. Hence, they are suitable for the promise of IoT of tagging and making just anything in the world to be wirelessly identifiable [1], [5], [6] and data exchangeable. Because of its low cost and low energy consumption, passive RFIDs are so popular that they are often regarded as RFID itself.…”

“…The existing algorithms are such that after a query cycle (read round), the reader uses information about collision slots to estimate the unidentified tags and predict a frame size for the next query cycle. Recently, tag grouping is becoming an interesting method towards enhancing DFSA [3], [5], [6], [8], [17], [28]. Tag grouping guarantees the possibility that tags access the shared channel at the same time with minimal collision [3].…”

A Review of Tags Anti-Collision Identification Methods Used in RFID Technology

“…An important advantage of RFID technology is the simultaneous identification of multiple targets. If you want to achieve multiple targets for simultaneous identification, we must solve multiple tags corresponding to a reader or multiple readers when there is a signal interference problem, namely, collision problem [104]. Tag collision refers to when more than one tag corresponds to a reader, and the tags simultaneously sends data to the reader, the signals collide with each other, so that the reader cannot correctly obtain the relevant information.…”