Intelligent Drone Positioning via BIC Optimization for Maximizing LPWAN Coverage and Capacity in Suburban Amazon...

Area of Science:

• Wireless Communication Systems
• Network Optimization
• Robotics and Automation

Background:

• Unmanned Aerial Vehicle (UAV) base stations offer flexible network deployment.
• Low-Power Wireless Area Networks (LPWAN), like LoRa, are suitable for extensive coverage with low data rates.
• Optimizing UAV positioning is crucial for maximizing coverage in complex environments.

Purpose of the Study:

• To present a metaheuristic approach for optimizing drone array placement.
• To maximize coverage area for wireless communication systems using UAV base stations.
• To analyze performance in suburban, wooded Amazonian urban environments.

Main Methods:

• Applied Low-Power Wireless Area Network (LPWAN) technology, specifically LoRa.
• Utilized three bio-inspired computing (BIC) methods: Cuckoo Search (CS), Flower Pollination Algorithm (FPA), and Genetic Algorithm (GA) for UAV positioning.
• Developed and validated an empirical propagation model for forested environments using MATLAB simulations.

Main Results:

• Optimized UAV positioning was simulated for high-range IoT-LoRa networks.
• An empirical propagation model for LoRa in forested areas (SF 8-11) was developed.
• Comparison between theoretical and measured propagation models for UAVs was conducted.

Conclusions:

• The metaheuristic approach effectively optimizes drone array placement for enhanced wireless coverage.
• The developed propagation model provides a more accurate representation of signal behavior in Amazonian urban forests.
• This research contributes to efficient deployment of UAV-based communication networks in challenging terrains.