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Energy Constrained Optimization for Spreading Factor Allocation in LoRaWAN.

Shusuke Narieda1, Takeo Fujii2, Kenta Umebayashi3

  • 1Graduate School of Engineering, Mie University, Mie 514-8507, Japan.

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Summary

This study optimizes spreading factor allocation in Long Range Wide Area Networks (LoRaWAN) to maximize packet reception probability (PRP) while constraining energy consumption for end devices (EDs). The proposed method outperforms existing techniques in both PRP and energy efficiency.

Keywords:
LoRaWANenergy consumptionspreading factor

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Area of Science:

  • Wireless Communication Networks
  • Internet of Things (IoT) Systems
  • Optimization Algorithms

Background:

  • Long Range Wide Area Network (LoRaWAN) utilizes chirp spread spectrum for low-power, long-range communication.
  • Existing spreading factor allocation techniques improve LoRaWAN performance but often neglect end-device energy consumption.
  • Optimizing spreading factor allocation is crucial for balancing performance and energy efficiency in LoRaWAN.

Purpose of the Study:

  • To present a novel spreading factor allocation technique for LoRaWAN.
  • To maximize packet reception probability (PRP) under a constrained average energy consumption for end devices (EDs).
  • To enhance overall LoRaWAN performance while managing energy usage.

Main Methods:

  • Defined an optimization problem to maximize PRP subject to average energy consumption constraints.
  • Developed a distributed genetic algorithm, a metaheuristic method, to solve the defined spreading factor allocation problem.
  • Validated the technique through numerical examples comparing its performance against existing methods.

Main Results:

  • The proposed spreading factor allocation technique effectively enhances LoRaWAN performance.
  • Achieved superior packet reception probability (PRP) compared to existing techniques.
  • Demonstrated lower average energy consumption for end devices (EDs) than previously reported methods.

Conclusions:

  • The presented technique successfully optimizes spreading factor allocation in LoRaWAN.
  • It provides a method to improve network performance (PRP) while adhering to energy consumption constraints.
  • The distributed genetic algorithm approach is effective for solving this complex optimization problem.