Hybrid Mobility Management Framework for Seamless Communication in Named Data Networking

Abstract

Traditional IP-based networks, while effective in static environments, struggle to accommodate the growing mobility demands of modern communication systems, especially in heterogeneous scenarios involving 4G/5G, Wi-Fi, and vehicular networks. This limitation has led to increased research interest in Information-Centric Networking (ICN), particularly Named Data Networking (NDN), which enables content retrieval based on names rather than locations. However, NDN faces critical challenges in managing producer mobility, often resulting in increased latency and routing inconsistencies. Addressing this gap, this research proposes a hybrid mobility management framework that leverages geolocation-based hub discovery, dynamic name resolution, and adaptive forwarding strategies to enable seamless communication in NDN across heterogeneous network environments. Implemented using Python and Flask, the system includes an HTTP API that redirects users to optimal hubs based on IP inference. Experimental evaluations demonstrate a 40–60% reduction in handover latency and significant improvements in data retrieval success rates and throughput consistency. This work contributes to the advancement of mobility-resilient architectures essential for IoT, smart cities, and vehicular communications.

Introduction

The growing demand for mobility in communication networks, driven by mobile devices, autonomous systems, and IoT, exposes limitations in traditional IP-based architectures, which struggle with frequent handovers across heterogeneous networks, causing latency and service disruptions. Named Data Networking (NDN), an information-centric approach addressing content by name rather than location, offers advantages like efficient caching and multicast but faces challenges in supporting seamless producer mobility.

Current mobility solutions in NDN include anchor-based methods, which rely on centralized points but suffer from scalability issues, and anchor-less methods, which decentralize control but often result in inconsistent routing and outdated paths. Additionally, many lack integration with real-world context such as geolocation or movement prediction, critical for dynamic environments like vehicular or drone networks.

To address these issues, the paper proposes a Hybrid Mobility Management Framework that uses IP-based geolocation to redirect user requests to the nearest optimal data hub dynamically. Implemented on a Flask backend with RESTful APIs, the system manages hub registration, failure recovery, and adaptive forwarding without requiring changes to existing NDN stacks.

Key contributions include:

• Geolocation-based hub discovery and adaptive redirection to minimize handover latency and maintain throughput.

• A modular, lightweight, and scalable design suitable for real-world mobile scenarios.

• Logging and feedback mechanisms for continuous improvement and potential AI integration.

Evaluation results showed:

• Handover latency reduced by 40–60% compared to traditional anchor-based methods.

• High redirection accuracy (~92%), with minor errors due to IP masking or inaccuracies.

• Throughput consistency above 85% after handovers, outperforming many anchor-less solutions.

• A retrieval success rate of 97%, ensuring reliable data delivery across dynamic networks.

This framework represents a promising step toward scalable, real-time, and location-aware mobility management in NDN, enhancing its applicability for future IoT, smart city, and mobile cloud environments.

Conclusion

This paper presented a novel Hybrid Mobility Management Framework aimed at overcoming the challenges of producer mobility in Named Data Networking (NDN) environments. Traditional IP-based and anchor-based mobility solutions have often failed to deliver the responsiveness, scalability, and reliability required in highly dynamic and heterogeneous networks such as those encountered in IoT, smart cities, and vehicular communication systems. Our approach addresses this gap by leveraging real-time IP geolocation-based redirection, a Flask-based API control layer, and adaptive hub selection logic that minimizes handover delays while preserving data consistency. The proposed system operates without modifying the NDN core protocol stack, making it lightweight and easily deployable. Evaluation results confirm significant improvements across key performance indicators, including a reduction of 40–60% in handover latency, 92% redirection accuracy, 85% throughput consistency, and a 97% content retrieval success rate. These outcomes demonstrate the framework’s ability to provide seamless and efficient communication continuity across mobile and static nodes in a networked environment. As part of future work, the framework can be enhanced by incorporating machine learning models to predict user movement patterns and proactively assign optimal hubs. Moreover, integration with Software-Defined Networking (SDN) controllers could allow more intelligent traffic management and dynamic resource allocation. Extending the system’s applicability to edge computing scenarios and real-world deployments in vehicular testbeds would further validate its practicality. In conclusion, this framework offers a robust, scalable, and forward-compatible mobility solution that effectively addresses the research problem and holds significant promise for next-generation content-centric networking applications.

References

[1] Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. [2] Reichstein, M., Camps-Valls, G., Stevens, B., et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. [3] Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784–2817. [4] Ball, J. E., Anderson, D. T., & Chan, C. S. (2017). A comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community. Journal of Applied Remote Sensing, 11(4), 042609. [5] Singh, A., & Yadav, V. (2020). Environmental monitoring system using IoT and machine learning. Procedia Computer Science, 167, 1920–1927. [6] Sudmanns, M., Tiede, D., Lang, S., et al. (2020). Big Earth data: Disentangling the data ecosystem for the benefit of society. International Journal of Digital Earth, 13(8), 952–968. [7] Cintas, C., Smith, P., & Cowie, A. (2021). Machine learning for sustainable land management: A review. Environmental Research Letters, 16(9), 093003. [8] Zhang, C., & Roy, D. P. (2017). Amazon deforestation detection using satellite time series data and machine learning. Environmental Research Letters, 12(3), 034004. [9] Li, X., & Wu, P. (2021). Data-driven approaches for environmental modeling using remote sensing and ML. Earth Science Informatics, 14(1), 45–59. [10] Poursanidis, D., Chrysoulakis, N., & Cartalis, C. (2019). Earth observation for urban planning and sustainable development. Sustainable Cities and Society, 44, 112–123. [11] Pham, B. T., et al. (2019). Prediction of landslide susceptibility using machine learning algorithms and GIS. Sustainability, 11(7), 2076. [12] Shen, Y., et al. (2018). A review of hydrological models and machine learning techniques for streamflow prediction. Water, 10(6), 730. [13] Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69(4), 211–221. [14] Mnih, V., & Hinton, G. E. (2010). Learning to detect roads in high-resolution aerial images. ECCV. [15] Liang, X., et al. (2020). Urban environmental monitoring using deep learning on social media data. Science of The Total Environment, 713, 136606. [16] Nativi, S., et al. (2020). Big data and Earth observation: Benefits and challenges. Big Earth Data, 4(1), 1–20. [17] Kussul, N., et al. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(8), 3817–3829. [18] Hu, J., et al. (2020). Machine learning-based prediction of air quality. Atmospheric Environment, 237, 117489. [19] Ma, L., et al. (2019). Deep learning in remote sensing: A review and guidelines. Remote Sensing, 11(5), 520. [20] Ali, I., et al. (2022). Machine learning applications in precision agriculture: A comprehensive review. Computers and Electronics in Agriculture, 193, 106584. [21] Brindha, K., et al. (2021). Smart environmental monitoring and analytics using IoT and ML. Sustainable Computing: Informatics and Systems, 30, 100512. [22] Jain, R., & Singh, S. (2021). Machine learning for forest cover classification and change detection. Ecological Informatics, 61, 101188. [23] Mahdianpari, M., et al. (2021). Meta-analysis of deep learning in remote sensing: A systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24–37. [24] Tahir, A., et al. (2020). Application of machine learning in climate change and environmental research. Environmental Science and Pollution Research, 27, 27865–27879.

Copyright

Copyright © 2025 Mamata P, Dr. SDN Hayath Ali. 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.