Smart skies: optimizing autonomous AUAV positioning for robust IoT connectivity in next-gen cities

Area of Science:

• Electrical Engineering
• Computer Science
• Telecommunications

Background:

• Next-generation smart cities require robust IoT connectivity, especially after disasters damage existing infrastructure.
• Aerial networking using unmanned aerial vehicles (UAVs) is crucial for maintaining connectivity in post-disaster scenarios.

Purpose of the Study:

• To propose a novel Define-Measure-Analyze-Improve-Control (DMAIC) enhanced framework for autonomous UAV positioning.
• To optimize UAV base station placement in 3D space for maximum coverage and energy efficiency in urban environments.

Main Methods:

• Simultaneous optimization of UAV's x-y coordinates and altitude using analytical partial-derivative optimization.
• Consideration of realistic urban channel conditions, including elevation-dependent path loss and mixed Rician/Rayleigh fading.

Main Results:

• The proposed DMAIC framework significantly outperforms fixed-altitude UAV deployment, achieving up to 55% improvement in energy efficiency and coverage probability.
• Compared to a state-of-the-art baseline, the DMAIC method offers 5-10% higher coverage, 10-15% better energy efficiency, lower latency, and higher aggregate throughput.

Conclusions:

• The DMAIC framework provides valuable design principles for robust, energy-efficient UAV-assisted networks in 6G smart cities, essential for emergency and recovery scenarios.
• Analytically guided, adaptive UAV repositioning is effective for maintaining connectivity in complex urban environments, particularly during network recovery.