An NDN IoT Content Distribution Model With Network Coding Enhanced Forwarding Strategy for 5G

Kai Lei, Shangru Zhong, Fangxing Zhu, Kuai Xu, Haijun Zhang

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


The challenging requirements of fifth-generation (5G) Internet-of-Things (IoT) applications have motivated a desired need for feasible network architecture, while Named Data Networking (NDN) is a suitable candidate to support the high density IoT applications. To effectively distribute increasingly large volumes of data in large-scale IoT applications, this paper applies network coding techniques into NDN to improve IoT network throughput and efficiency of content delivery for 5G. A probability-based multipath forwarding strategy is designed for network coding to make full use of its potential. To quantify performance benefits of applying network coding in 5G NDN, this paper integrates network coding into a NDN streaming media system implemented in the ndnSIM simulator. The experimental results clearly and fairly demonstrate that considering network coding in 5G NDN can significantly improve the performance, reliability, and QoS. Besides, this is a general solution as it is applicable for most cache approaches. More importantly, our approach has promising potentials in delivering growing IoT applications including high-quality streaming video services.

Original languageEnglish (US)
Pages (from-to)2725-2735
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number6
StatePublished - Jun 2018


  • Content delivery
  • internet-of-things (IoT) streaming system
  • named data networking (NDN)
  • network coding
  • probability multipath forwarding

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'An NDN IoT Content Distribution Model With Network Coding Enhanced Forwarding Strategy for 5G'. Together they form a unique fingerprint.

Cite this