LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions.

Area of Science:

• Telecommunications Engineering
• Wireless Communication Systems
• Environmental Radio Science

Background:

• Deploying wireless networks in the Amazon presents unique challenges due to its complex terrain.
• Low-Power Wide-Area Networks (LPWANs) are essential for Internet of Things (IoT) applications in remote areas.

Purpose of the Study:

• To develop and validate large-scale propagation loss models for LoRaWAN (915 MHz) in the Amazon region.
• To facilitate the planning of mobile IoT networks in riverside communities.
• To assess channel quality for LoRa physical layer (PHY) communications.

Main Methods:

• Conducted extensive field measurements of Received Signal Strength Indication (RSSI) and Signal-to-Noise Ratio (SNR) along riverbanks.
• Fitted empirical Close-In (CI) and Floating Intercept (FI) propagation models for uplink path loss.
• Compared CI, FI, and Okumura-Hata models, and introduced a new model for dense vegetation.
• Analyzed received packet rate statistics to evaluate channel quality.

Main Results:

• CI and FI models showed similar performance; a new proposed model demonstrated superior accuracy for dense vegetation scenarios.
• The new model achieved lower Root Mean Square Error (RMSE) compared to Okumura-Hata, especially at Spreading Factor 9 (SF9).
• Determined a radius coverage threshold of 945 m, considering end-node mobility.

Conclusions:

• The proposed propagation model enhances the accuracy of radio loss prediction in densely vegetated Amazonian environments.
• Findings provide crucial data for optimizing LoRaWAN-based IoT network deployment in challenging terrains.
• The study supports effective planning for resilient IoT connectivity in remote riverside communities.