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Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review

Author

Listed:
  • Wan Muhd Hazwan Azamuddin

    (Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Azana Hafizah Mohd Aman

    (Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Hasimi Sallehuddin

    (Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Maznifah Salam

    (Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Khalid Abualsaud

    (Department of Computer Science & Engineering, College of Engineering, Qatar University, Doha 2713, Qatar)

Abstract

One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies’ strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments.

Suggested Citation

  • Wan Muhd Hazwan Azamuddin & Azana Hafizah Mohd Aman & Hasimi Sallehuddin & Maznifah Salam & Khalid Abualsaud, 2024. "Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review," Mathematics, MDPI, vol. 12(5), pages 1-33, February.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:649-:d:1344217
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    References listed on IDEAS

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    1. Luís Gameiro & Carlos Senna & Miguel Luís, 2020. "ndnIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks," Future Internet, MDPI, vol. 12(11), pages 1-21, November.
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    1. Luís Gameiro & Carlos Senna & Miguel Luís, 2022. "Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments," Future Internet, MDPI, vol. 14(7), pages 1-18, June.

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