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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...

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Related Experiment Videos

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

Abdu Saif¹, Nor Shahida Mohd Shah², Weiwei Jiang³

¹Centre of Advanced Communication Research and Innovation (ACRI), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia. saif.abduh2016@gmail.com.

Scientific Reports

|July 14, 2026

Summary

This study introduces a DMAIC framework for autonomous drone positioning to ensure reliable IoT connectivity in smart cities after disasters. The method optimizes drone placement for better coverage and energy efficiency, outperforming existing strategies.

Keywords:

6G networks Autonomous UAV Coverage optimization DMAIC Energy efficiency IoT connectivity

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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.