engleski

Machine Learning as Tag Estimation Method for ALOHA- based RFID system

Massive implementation of Machine Learning (ML) techniques in different domains of IoT brought many different advantages that enhance performances in different usage scenarios. In this paper, use-case feasibility analysis of implementation of ML algorithm for estimating ALOHA-based frame size in Radio Frequncy Identification (RFID) Gen2 system is provided. The results show that the given ML algorithm can be employed on modern state-of-the-art resource constrained microcontrollers where execution time is enough to meet protocol needs, keep-up with the latency and improve system throughput.

Internet of Things , RFID tags , RFID reader , Machine Learning , tag estimate method

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