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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Adaptive Parameters for LoRa-Based Networks Physical-Layer.

Edelberto Franco Silva1, Lucas M Figueiredo2, Leonardo A de Oliveira1

  • 1Department of Computer Science, Federal University of Juiz de Fora (UFJF), Juiz de Fora 36036-330, MG, Brazil.

Sensors (Basel, Switzerland)
|July 11, 2023
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Summary
This summary is machine-generated.

SlidingChange dynamically adjusts LoRa (Long-Range) network parameters for better performance. This cognitive mechanism improves Signal-to-Noise Ratio (SNR) by 37% while reducing network reconfigurations by 16%.

Keywords:
IoTLoRaSub-GHzwireless cognitive radiowireless network

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Area of Science:

  • Wireless Communication
  • Internet of Things (IoT)
  • Network Performance Optimization

Background:

  • Sub-GHz communication, exemplified by LoRa (Long-Range), offers long-range, low-power connectivity for outdoor IoT devices.
  • LoRa technology's adaptability relies on dynamic parameter adjustments (frequency, bandwidth, spreading factor, code rate).
  • Existing methods for parameter adjustment, like InstantChange and LR-ADR, have limitations in balancing performance and reconfiguration frequency.

Purpose of the Study:

  • To introduce SlidingChange, a novel cognitive mechanism for dynamic analysis and adjustment of LoRa network performance parameters.
  • To evaluate SlidingChange's effectiveness in improving Signal-to-Noise Ratio (SNR) and reducing network reconfiguration rates.
  • To compare SlidingChange against InstantChange and LR-ADR in a practical testbed scenario.

Main Methods:

  • Development of the SlidingChange mechanism, utilizing a sliding window to smooth parameter variations and minimize unnecessary reconfigurations.
  • Experimental validation using a testbed to assess SlidingChange's impact on SNR and reconfiguration rate.
  • Comparative analysis against InstantChange (immediate parameter adjustments) and LR-ADR (linear regression-based technique).

Main Results:

  • InstantChange improved SNR by 4.6% compared to baseline.
  • SlidingChange achieved a significant SNR improvement of approximately 37%.
  • SlidingChange reduced the network reconfiguration rate by approximately 16% compared to InstantChange.

Conclusions:

  • SlidingChange offers a superior approach to optimizing LoRa network performance by balancing SNR improvements with reduced reconfiguration frequency.
  • The cognitive mechanism effectively smooths short-term variations, leading to more stable and efficient LoRa network operations.
  • SlidingChange presents a promising solution for enhancing the reliability and efficiency of wide-area IoT networks.