Quality of Service Assessment Using Crowdsourcing and Its Efficacy with Respect to Conventional Drive Testing

Abstract

As the demand for high-quality mobile network connectivity intensifies, telecom network engineers increasingly seek innovative approaches for Quality of Service (QoS) assessment. This research paper investigates the efficacy of crowdsourcing techniques in evaluating QoS and compares these methods with the traditional drive testing approach. The study is guided by clear objectives and research questions that address the operational, geographical, temporal, and technological boundaries of contemporary telecom networks. By employing crowdsourced data from user mobile devices alongside conventional drive test measurements, the study evaluates data quality, spatial-temporal resolution, and cost-effectiveness. Findings suggest that while drive testing offers controlled and standardized measurements, crowdsourcing provides extensive coverage, tapping into diverse user environments that capture authentic network performance variations. The paper concludes by discussing the potential for hybrid methodologies, integrating both approaches, and lays a roadmap for further research that can refine QoS assessment practices in telecom.

Introduction

This paper presents a comparative study between traditional drive testing and crowdsourcing methods for assessing Quality of Service (QoS) in modern telecom networks. With the rise of mobile data dependency and 5G technologies, the need for effective, scalable, and cost-efficient performance monitoring tools has grown significantly.

Key Points:

1. Traditional Drive Testing

• Involves specialized vehicles and standardized tools.

• Highly accurate and controlled measurements.

• Limited in geographic coverage, costly, and labor-intensive.

• Cannot easily capture dynamic, real-time user behavior or performance variability.

2. Crowdsourcing-Based QoS Assessment

• Leverages everyday mobile users and devices to collect performance data (signal strength, latency, etc.) via apps.

• Offers real-time, broad spatial and temporal coverage.

• Cost-effective, scalable, and ideal for capturing user-centric performance issues.

• Challenges include data variability, device heterogeneity, privacy concerns, and inconsistent reliability.

3. Comparative Findings

• Spatial Coverage: Crowdsourcing covers more diverse regions, especially where drive tests are limited. It identifies micro-level issues often missed by drive tests.

• Temporal Resolution: Crowdsourcing captures fine-grained temporal changes (e.g., peak-hour congestion), while drive testing provides stable, controlled snapshots.

• Data Quality: Drive testing provides more accurate but limited data. Crowdsourcing yields variable-quality data, which improves with filtering and machine learning corrections.

• Cost Efficiency: Crowdsourcing is significantly cheaper and easier to scale; drive testing incurs high costs due to hardware, personnel, and logistics.

• Statistical Comparison: Crowdsourcing data showed higher variability, especially during peak hours, but processed crowdsourced data approached drive test accuracy.

4. Hybrid Approach Recommendation

A hybrid model combining both methods is proposed:

• Use drive testing for calibration and validation.

• Use crowdsourcing for continuous, large-scale monitoring.

• Machine learning can reconcile data discrepancies and enhance reliability.
  This approach balances cost, accuracy, and coverage, enabling better resource allocation and infrastructure planning.

5. Implications for Network Engineers

• Enables better decision-making for network optimization and upgrades.

• Helps manage network demand during peak times and quickly detect service degradations.

• Particularly valuable in the 4G-to-5G transition, where network complexity and variability are higher.

6. Future Outlook

• Crowdsourcing is well-positioned to support 5G’s real-time and hyper-connected demands.

• Drive testing will require adaptation (e.g., for mmWave and small cells).

• Regulatory, ethical, and privacy challenges with crowdsourced data must be addressed for widespread adoption.

Conclusion

This paper has presented a detailed comparative analysis of crowdsourcing techniques versus conventional drive testing for the assessment of QoS in telecom networks. Our findings indicate that while drive testing remains indispensable for obtaining high-precision measurements in controlled environments, crowdsourcing offers significant advantages in terms of geographic coverage, temporal granularity, and cost-effectiveness. By capturing real-user data across varied settings, crowdsourcing provides insights into network performance that drive testing alone cannot deliver. The integration of both methods into a hybrid framework has the potential to revolutionize QoS monitoring, providing telecom network engineers with a richer and more responsive dataset for performance diagnostics. As operators grapple with the challenges posed by the deployment of 5G networks and beyond, innovative QoS assessment strategies will play an increasingly critical role in maintaining high levels of service quality and customer satisfaction. Future work should focus on refining data reconciliation techniques, expanding the geographic reach of crowdsourced data, and developing advanced privacy-preserving mechanisms to encourage broader user participation. The findings presented herein underscore the importance of collaboration between network operators, researchers, and app developers to continuously improve network performance monitoring strategies. In conclusion, the comparative study highlights that a balanced integration of crowdsourcing and drive testing methodologies is not only feasible but also beneficial in addressing the complexities of modern telecom network environments. The lessons learned from this research provide a robust foundation for future improvements and innovations in the field of QoS assessment.

References

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Copyright

Copyright © 2025 Sikander Hayat Sarmad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.