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Robotic Sensing and Stimuli Provision for Guided Plant Growth

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LoRaBB: An Algorithm for Parameter Selection in LoRa-Based Communication for the Amazon Rainforest.

Diogo Soares Moreira¹, Gilmara Santos¹, Angela Emi Yanai²

¹Institute of Computing (IComp), Federal University of Amazonas, Manaus 69080-900, Brazil.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This study introduces a new algorithm for selecting LoRa transmission parameters, reducing transmission time and energy consumption for efficient Internet of Things (IoT) environmental monitoring.

Keywords:

Internet of Things LoRa LoRa transmission parameter selection single-channel LoRa gateways

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Area of Science:

Environmental Science
Computer Science
Electrical Engineering

Background:

Human activities degrade water quality, necessitating effective environmental monitoring.
The Internet of Things (IoT) and LoRa (Long Range) offer low-cost, long-distance monitoring solutions.
Optimizing LoRa transmission parameters is crucial for efficient IoT data exchange in dynamic environments.

Purpose of the Study:

To develop a novel algorithm for selecting LoRa transmission parameters.
To enhance data transmission efficiency and reduce energy consumption in IoT environmental monitoring.
To validate the algorithm's performance in a forest environment.

Main Methods:

Incorporated LoRa metrics: Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet Delivery Ratio (PDR).
Conducted comprehensive characterization and validation in a forest environment to establish reference transmission quality values.
Employed a binary search methodology using an R-array to represent transmission quality based on LoRa parameters.

Main Results:

Achieved a 16.20% reduction in Time on Air (ToA).
Reduced energy consumption by at least 38% compared to Adaptive Data Rate (ADR) transmission power parameters.
Demonstrated enhanced transmission performance in a rainforest environment.

Conclusions:

The proposed algorithm significantly improves LoRa transmission efficiency.
Optimized parameter selection leads to substantial reductions in ToA and energy consumption.
The algorithm is effective for enhancing IoT-based environmental monitoring in challenging terrains like rainforests.