Towards a High Efficiency of Native NDN over Wi-Fi 6 for the Internet of Vehicles

  • Ygor Amaral B. L. de Sena UFPE / UFRPE
  • Kelvin Lopes Dias UFPE


Named Data Networking (NDN) is a top-notched architecture to deal with content distribution over the Internet. With the explosion of video streaming transmission and future massive Internet of Things and Vehicles (IoT/IoV) traffic, evolving Wi-Fi networks will play an essential role in such ecosystems. However, Native NDN deployment over wireless networks may not perform well. Wi-Fi broadcasts/multicasts result in reduced throughput due to the usage of basic service mode. Despite recent initial works addressing that issue, further studies and proposals are required to boost the adoption of Native NDN. We advocate that an initial step towards designing a feasible Native NDN over wireless networks should be understanding the challenges in emerging scenarios and providing a uniform baseline to compare and advance proposals. To this end, first, we highlight some challenges and directions to improve throughput and energy efficiency, reduce processing overhead, and security issues. Next, we propose a variant of NDN that minimizes the problems identified by performing transmission via unicast to avoid storms in wireless networks. Finally, we conducted a performance evaluation to compare Standard Native NDN with our proposal on Wi-Fi 6 vehicular networks. The results show that our proposal outperforms the Standard NDN in the evaluated scenarios, reaching values close to 89% of satisfied requests, achieving more than 200% of data received than Standard NDN.


Afanasyev, A. et al. (2018). NFD Developer’s Guide. Technical Report NDN-0021.

Anastasiades, C., Weber, J., and Braun, T. (2016). Dynamic Unicast: Information-centric multi-hop routing for mobile ad-hoc networks. Computer Networks, 107:208–219. Mobile Wireless Networks.

Arcuri, A. and Briand, L. (2011). A Practical Guide for Using Statistical Tests to Assess Randomized Algorithms in Software Engineering. In 2011 33rd International Conference on Software Engineering (ICSE), pages 1–10.

Coutinho, R. W. L., Boukerche, A., and Yu, X. (2018). A Novel Location-Based Content Distribution Protocol for Vehicular Named-Data Networks. In 2018 IEEE Symposium on Computers and Communications (ISCC), pages 01007–01012.

CTTU (2019). Open Data of Vehicle Traffic from Recife–Brazil. Available in: [link].

de Sena, Y. A. B. L. and Dias, K. L. (2022). Native versus Overlay-based NDN overWi-Fi 6 for the Internet of Vehicles. In Jiang, D. and Song, H., editors, Simulation Tools and Techniques. SIMUtools 2021, volume 424, pages 51–63, Cham. Springer International Publishing.

de Sena, Y. A. B. L., Dias, K. L., and Zanchettin, C. (2020). DQN-AF: Deep Q-Network based Adaptive Forwarding Strategy for Named Data Networking. In 2020 IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.

Duarte, J. M., Braun, T., and Villas, L. A. (2019). MobiVNDN: A distributed framework to support mobility in vehicular named-data networking. Ad Hoc Networks, 82:77–90.

Gomez, C., Kovatsch, M., Tian, H., and Cao, Z. (2018). Energy-Efficient Features of Internet of Things Protocols. RFC 8352.

Grassi, G., Pesavento, D., Pau, G., Vuyyuru, R., Wakikawa, R., and Zhang, L. (2014). VANET via Named Data Networking. In 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 410–415.

IEEE SA (2021). IEEE Standard for Information Technology – Telecommunications and Information Exchange between Systems - Local and Metropolitan Area Networks– Specific Requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Std 802.11-2020 (Revision of IEEE Std 802.11-2016), pages 1–4379.

Khorov, E., Kiryanov, A., Lyakhov, A., and Bianchi, G. (2019). A Tutorial on IEEE 802.11ax High Efficiency WLANs. IEEE Communications Surveys Tutorials, 21(1):197–216.

Kietzmann, P., Gündogan, C., Schmidt, T. C., Hahm, O., and Wählisch, M. (2017). The Need for a Name to MAC Address Mapping in NDN: Towards Quantifying the Resource Gain. In 4th ACM Conference on Information-Centric Networking (ICN ’17), pages 36–42.

Li, M., Pei, D., Zhang, X., Zhang, B., and Xu, K. (2015). NDN Live Video Broadcasting over Wireless LAN. In 2015 24th International Conference on Computer Communication and Networks (ICCCN), pages 1–7.

Liang, T., Pan, J., Rahman, M. A., Shi, J., Pesavento, D., Afanasyev, A., and Zhang, B. (2020). Enabling Named Data Networking Forwarder to Work Out-of-the-Box at Edge Networks. In 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1–6.

Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wiessner, E. (2018). Microscopic Traffic Simulation using SUMO. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2575–2582.

Mastorakis, S., Afanasyev, A., and Zhang, L. (2017). On the Evolution of ndnSIM: an Open-Source Simulator for NDN Experimentation. SIGCOMM Comput. Commun. Rev., 47(3):19–33.

Nour, B., Li, F., Khelifi, H., Moungla, H., and Ksentini, A. (2019). Coexistence of ICN and IP Networks: An NFV as a Service Approach. In 2019 IEEE Global Communications Conference (GLOBECOM), pages 1–6.

ns-3 (2021). ns-3 Network Simulator Website. Available in:

Rothmuller, M. and Barker, S. (2020). IoT - The Internet of Transformation 2020. pages 1–8, Basingstoke, UK. Juniper Research.

Shi, J., Newberry, E., and Zhang, B. (2017). On Broadcast-based Self-Learning in Named Data Networking. In 2017 IFIP Networking Conference (IFIP Networking) and Workshops, pages 1–9.

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P.,Weckesser,W., Bright, J., van derWalt, S. J., Brett, M.,Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17:261–272.

Wang, X., Wang, Z., and Cai, S. (2020). Data Delivery in Vehicular Named Data Networking. IEEE Networking Letters, 2(3):120–123.

Wu, F., Yang, W., Fan, Z., and Tian, K. (2018). Multicast Rate Adaptation in WLAN via NDN. In 2018 27th International Conference on Computer Communication and Networks (ICCCN), pages 1–8.

Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., claffy, k., Crowley, P., Papadopoulos, C., Wang, L., and Zhang, B. (2014). Named Data Networking. SIGCOMM Comput. Commun. Rev., 44(3):66–73.
Como Citar

Selecione um Formato
SENA, Ygor Amaral B. L. de; DIAS, Kelvin Lopes. Towards a High Efficiency of Native NDN over Wi-Fi 6 for the Internet of Vehicles. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 601-614. ISSN 2177-9384. DOI: