An anti-collision algorithm for large-scale RFID in noisy environments applied to the Industrial Internet of Things
Industrial IoT, GSPN, RFID, DFSA, Performability, Error Modeling.
The Industrial Internet of Things (IIoT) is often presented as a concept that is significantly changing the technological landscape of industries, through automation procedures and identification of relevant objects. For this, however, reliability and performance issues must be considered when providing anticipated communication services. By employing Radio Frequency Identification (RFID) in the IIoT context, several previous studies have worked to improve the efficiency of RFID communications systems, generally defining mathematical models for planning and evaluating quality. However, such models are designed based on error-free communications, which is in fact unrealistic when considering the error-prone nature of wireless communications in industrial plants. Therefore, this thesis proposes a new anti-collision algorithm for RFID together with a formal model based on Generalized Stochastic Petri Nets (GSPN) to evaluate RFID communications, modeling different possibilities of errors between readers and RFID tags. Since this proposal uses the EPCglobal UHF Class 1 Gen2 parameters as a reference, which are already adopted by the Dynamic Frame Slotted Aloha anti-collision protocol for passive RFID systems, this model can be explored to assess the performance of different RFID access protocols when assuming noisy channels, supporting better comparisons between different algorithms and protocols. The results showed that the proposed algorithm is able to present a better performability in relation to the other evaluated protocols, mainly in the presence of noisy channels if a large number of tags to be read. Simulation scenarios are defined to provide reliability and performance results when evaluating RFID tag readings, which are valuable when designing and maintaining IIoT applications.