Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

Abstract

This paper focuses on the use of radio frequency signals for non-contact tracking and localization utilising Radio Frequency Identification (RFID) technology. The clustering approach is quite useful when dealing with large RFID readers. This method stimulates the expansion of RFID nodes without compromising the overall network performance. The Cluster Head (CH) is the most critical node in a clustered RFID system. This research presents a method for dynamically selecting the cluster head that takes the connectivity and power of each RFID reader into account. In dynamic mode, selecting a new cluster head is based on Fuzzy Logic. Based on these data, the energy level of 0.443 and centrality of 0.809 are the thresholds at which a node has a 91.8 % chance of becoming a CH. The management of massive RFID networks is also handled, with cluster numbers increasing at different time intervals. The RFID network's effectiveness is determined by measuring throughput, accuracy, delay, success, and error rates. The network may accommodate up to a thousand nodes with 13 node leaders for improved capacity. The results show a 97.8 % success rate, 0.22 % accuracy, a 2.64 % error rate, and a 36.91 second latency.

Enhancing Laboratory Experience: A Combination of RFID and Machine Learning Techniques to Track Attendance and Upgrade Hardware

“…Additionally, RFID tag material will be detected far past the craft of contact, whereas barcodes must be aligned with a laser sensor. The phrase "away reading" refers to a group of methods that use observing the ground in addition to telescopes in addition to space-based technologies for enhanced detail and precision [2]. RFID tags come in two varieties ,they are passive ones and RFID tags that run on batteries.…”