Issue |
ITM Web Conf.
Volume 52, 2023
International Conference on Connected Object and Artificial Intelligence (COCIA’2023)
|
|
---|---|---|
Article Number | 03004 | |
Number of page(s) | 9 | |
Section | Telecommunications | |
DOI | https://doi.org/10.1051/itmconf/20235203004 | |
Published online | 08 May 2023 |
Distributed Rendezvous placement for producer mobility support in NDN-IoT
1 National Institute of Posts and Telecommunications Rabat, Morocco
2 Ecole Nationale des Sciences Appliquées de Berrechid, University of Hassan I Settat, Morocco
* Corresponding author: bellaj.mohammed@inpt.ac.ma
Named Data Networking is a new networking architecture proposed as a potential design for the next generation of networks. However, supporting the mobility of producers in NDN-based IoT systems remains an open challenge for researchers to address. To tackle this problem, the NDN research group introduced a solution called KITE [1]. Since this solution is an initial design, some issues to be resolved remain, including the rendezvous placement selection. The location of the rendezvous servers can significantly reduce the path stretch efficiently. This paper focuses on improving the KITE solution by addressing two specific issues. First, we aim to determine the optimal number of rendezvous servers to use in the network. Second, we seek to identify the best locations for the rendezvous servers to minimize the path stretch efficiently. We propose an evaluation metric called average hop count based on network centrality measures to select the number and the location for the rendezvous. We evaluate the Average hop count under different network centrality measures using random scale-free network topology. Our results show that the betweenness centrality measure performs better than other measures, and we, therefore, suggest using it as a metric for selecting the number of rendezvous servers and their locations.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.