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This study analyzes Radio-Frequency Identification (RFID) systems using stochastic Petri nets to optimize performance for edge computing. Findings reveal the best balance between latency and throughput for dependable RFID object identification.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Environmental Science

Background:

  • The shift from centralized cloud computing to edge, fog, and Internet of Things (IoT) architectures brings computation closer to end-users.
  • Mobile Network Operators (MNOs) are adopting 5G, driving enterprise computing functions towards the network edge.
  • Environmental concerns necessitate the development of IoT devices to mitigate the greenhouse effect of current applications.

Purpose of the Study:

  • To examine the availability, dependability, and latency of Radio-Frequency Identification (RFID) tag-based object-identification systems.
  • To identify the optimal balance between system latency and throughput for critical applications.
  • To assess the viability of RFID systems deployed at the edge layer for environmental applications.

Main Methods:

  • Stochastic Petri Nets (SPNs) were employed to model and analyze the RFID system.
  • Multiple communication scenarios were simulated and evaluated.
  • Performance metrics including availability, dependability, and latency were quantified.

Main Results:

  • An optimal trade-off between latency and throughput was determined for the RFID system.
  • The analysis confirmed the system's availability when deployed within the edge computing layer.
  • Specific communication scenarios demonstrated the system's suitability for edge deployments.

Conclusions:

  • RFID technology offers potential for developing environmentally friendly IoT applications.
  • Stochastic Petri Net analysis provides a robust method for evaluating critical performance aspects of RFID systems.
  • The study validates the feasibility and performance of RFID-based object identification at the network edge.