Named data networking architecture for internet of vehicles in the era of 5G.

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Date: Oct. 2021
From: Annales des Telecommunications(Vol. 76, Issue 9-10)
Publisher: Springer
Document Type: Report; Brief article
Length: 298 words

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Keywords: Internet of vehicles; Named data networking; ROOF standard; Machine learning; Searchable encryption; Smart cities; Intelligent transportation systems Abstract Things are interconnected using information and communication technologies in smart cities, forming Internet of Things (IoT). The Internet of Vehicles (IoV) refers to an IoT application, where the urban vehicle fleet forms a worldwide network, using V2X (Vehicle-to-Everything) communications. The 5G is the new generation of cellular networks that will eliminate the bounds of bandwidth, performance, and latency limitations. IoV is one of the high-priority application domains for 5G. Among the under development IEEE Standard regarding 5G, the IEEE P1931.1 standard (named also Real-time Onsite Operations Facilitation (ROOF) Standard) seems to be very promising for IoV requirements. This paper proposes ROOF-based Named Data Vehicular Networking (RND - V.sub.n), a named data networking (NDN) architecture for IoV. In addition to the proposal, we provide SeCrND.sub.n (Searchable Encryption for Content Retrieval in NDN), a searchable encryption technique for NDN content retrieval. Furthermore, we propose the intelligent Named Data Caching (iNDC), a machine-learning--based data caching technique for ROOF-based named data networking. The iNDC predicts the number of content requests, such that popular contents are kept as long as possible on roadside units. The proposed iNDC is also used to predict the storage capacity required by each roadside unit. A performance study was conducted to evaluate the performance of machine learning algorithms applied to iNDC. The results show that linear and ridge regressions are the most efficient in terms of content popularity prediction. To predict the capacity of new roadside units, iNDC provides better accuracy using k-Nearest Neighbors. Author Affiliation: (1) Ecole Nationale Superieure de Technologie (ENST), Algiers, Algeria (2) Universite Paris-Est, Champs sur Marne, France (a) ab_kaci@esi.dz Article History: Registration Date: 06/28/2021 Received Date: 10/02/2020 Accepted Date: 06/25/2021 Online Date: 07/21/2021 Byline:

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Gale Document Number: GALE|A679580256