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Adaptive Algorithms for Batteryless LoRa-Based Sensors.

Fabrizio Giuliano1,2, Antonino Pagano1,2, Daniele Croce1,2

  • 1Department of Engineering, University of Palermo, 90128 Palermo, Italy.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study optimizes batteryless Internet-of-Things (IoT) sensors using adaptive LoRa transmission. We analyzed power consumption and tuned network parameters for improved performance and reliability in energy-harvesting systems.

Keywords:
LoRaadaptive algorithmsbatterylessenergy harvestinginternet of thingswireless sensor networks

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Area of Science:

  • Wireless Sensor Networks
  • Energy Harvesting Systems
  • Internet-of-Things (IoT)

Background:

  • Batteryless sensors offer sustainable, cost-effective IoT solutions with reduced maintenance.
  • The Internet-of-Things (IoT) relies on energy-efficient sensors for widespread deployment.
  • LoRa protocol is a key technology for long-range, low-power IoT communication.

Purpose of the Study:

  • To enhance the performance of batteryless IoT sensors powered by ambient energy.
  • To investigate adaptive transmission algorithms for LoRa-based batteryless sensors.
  • To optimize energy storage and minimize energy failures in sustainable IoT devices.

Main Methods:

  • Characterized device power consumption during sensor measurement and transmission.
  • Dynamically tuned network parameters (transmission time, data redundancy, packet size).
  • Designed capacity-based storage for renewable energy sources and analyzed energy failure probabilities using theoretical models and real traces.

Main Results:

  • Optimized operation of batteryless IoT devices through adaptive transmission algorithms.
  • Quantified energy failure probabilities based on energy source characteristics and storage capacity.
  • Provided a cost analysis for system components, including capacitors and solar panels.

Conclusions:

  • Adaptive transmission algorithms significantly improve batteryless IoT sensor performance.
  • Proper energy storage dimensioning is crucial for meeting reliability constraints in energy-harvesting IoT systems.
  • The findings guide the design of more robust and cost-effective sustainable IoT solutions.