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Downlink Performance Modeling and Evaluation of Batteryless Low Power BLE Node.

Ashish Kumar Sultania1, Carmen Delgado2, Chris Blondia1

  • 1IDLab-Department of Computer Science, University of Antwerp-imec, 2000 Antwerp, Belgium.

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Summary

This study models batteryless Internet of Things (IoT) devices using Bluetooth Low Energy (BLE) Low Power Nodes (LPNs). It analyzes performance based on energy harvesting and capacitor size to optimize data latency and packet delivery for long-life IoT systems.

Keywords:
Bluetooth Low EnergyIoTbatterylessenergy harvestinglow power node

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Area of Science:

  • Wireless Communication Systems
  • Internet of Things (IoT)
  • Energy Harvesting Technologies

Background:

  • Emerging commercial Internet of Things (IoT) solutions require low-maintenance, long-life systems.
  • Batteryless IoT devices powered by ambient energy harvesting and long-lifetime capacitors offer a sustainable alternative to traditional batteries.
  • Unpredictable energy availability can hinder communication initiation in batteryless IoT devices.

Purpose of the Study:

  • To present an analytical model for characterizing the performance of batteryless IoT devices using Bluetooth Low Energy (BLE) Low Power Nodes (LPNs).
  • To analyze the impact of different energy harvesting powers and capacitor sizes on downlink (DL) data latency and packet delivery ratio (PDR).
  • To provide insights for optimally configuring batteryless LPNs for network deployment.

Main Methods:

  • Development of an analytical model to evaluate the performance of batteryless LPNs.
  • Characterization of performance metrics including DL data latency and PDR.
  • Simulation of the system to validate the analytical model's predictions.

Main Results:

  • The analytical model accurately predicts DL data latency and PDR for batteryless LPNs.
  • Model and simulation results show high consistency, with an average error of 2.23% for DL data latency and 0.09% for PDR.
  • The study quantifies the trade-offs between energy harvesting, capacitor size, and communication performance.

Conclusions:

  • The proposed analytical model is effective for understanding and optimizing the performance of batteryless, energy-harvesting IoT devices using BLE LPNs.
  • Optimal configuration of harvesting power and capacitor size is crucial for achieving desired DL data latency and PDR.
  • This work facilitates the deployment of sustainable and long-life IoT solutions.