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Modeling of Packet Error Rate Distribution Based on Received Signal Strength Indications in OMNeT++ for Wake-Up

Mohamed Khalil Baazaoui1,2, Ilef Ketata1,2, Ahmed Fakhfakh2

  • 1Department of Electrical Engineering and Information Technology, University of Applied Sciences, 04107 Leipzig, Germany.

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Summary

This study models link quality metrics for wake-up receivers (WuRx) in wireless sensor networks (WSNs). Machine learning predicts packet error rate (PER) and received signal strength indicator (RSSI) for reliable network simulation.

Keywords:
OMNeT++packet error ratereceived signal strengthwake-up receiverwireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communication

Background:

  • Energy-saving wireless sensor networks (WSNs) are crucial for long-term monitoring and embedded applications.
  • Wake-up receiver (WuRx) technology enhances power efficiency in sensor nodes without increasing latency.
  • Real-world deployment of WuRx requires simulation considering environmental factors affecting network reliability.

Purpose of the Study:

  • To model link quality metrics for WuRx-based WSNs.
  • To integrate hardware (RSSI) and software (PER) metrics into the OMNeT++ discrete event simulator.
  • To evaluate network performance before real-world deployment.

Main Methods:

  • Developed machine learning regression models to characterize the behavior of SPIRIT1 transceivers.
  • Modeled Received Signal Strength Indicator (RSSI) as a hardware metric.
  • Modeled Packet Error Rate (PER) using machine learning, incorporating parameters like sensitivity and transition interval.

Main Results:

  • Successfully integrated hardware and software link quality metrics into the OMNeT++ simulator.
  • The machine learning models accurately predicted PER variations, aligning with real experimental outputs.
  • The generated simulation module effectively detects PER distribution changes.

Conclusions:

  • The study provides a reliable method for simulating WuRx-based WSNs by incorporating realistic link quality metrics.
  • The developed machine learning models enhance the accuracy of network simulations.
  • This approach facilitates robust WSN design and deployment by enabling pre-evaluation of network performance.