LoRaBB: An Algorithm for Parameter Selection in LoRa-Based Communication for the Amazon Rainforest
The interference of human activities in water bodies has contributed to a deterioration in water quality. With the advancement of the Internet of Things (IoT), aided by transmission technologies such as LoRa (Long Range), low-cost solutions have emerged for long-distance environment monitoring scena...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/4/1200 |
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| Summary: | The interference of human activities in water bodies has contributed to a deterioration in water quality. With the advancement of the Internet of Things (IoT), aided by transmission technologies such as LoRa (Long Range), low-cost solutions have emerged for long-distance environment monitoring scenarios. One key challenge in such IoT-based systems is selecting LoRa transmission parameters to ensure efficient data exchange among nodes, adapting to varying network conditions. Well-known strategies adapt transmission parameters according to network context through information exchange among nodes and LoRa gateway(s). In this work, we introduce a novel LoRa parameter selection algorithm by incorporating three major LoRa metrics (RSSI, SNR, and PDR) and conducting a comprehensive characterization and validation in the forest environment to build a set of reference values of transmission quality, which are employed in a binary search methodology, utilizing the <i>R</i>-array, representing the transmission quality according to LoRa parameters. The experimental results indicate that the proposed algorithm achieves a 16.20% reduction in Time on Air (ToA). Furthermore, our algorithm optimized the transmission power (TP) selection, achieving at least 38% lower energy consumption than ADR TP parameters. These results highlight that our proposed algorithm can enhance the transmissions in a rainforest environment. |
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| ISSN: | 1424-8220 |