CNN: A Cluster-Based Named Data Routing for Vehicular Networks

Saeid Pourroostaei Ardakani,Qianyu Liu,Lincan Li,Chiew Foong Kwong,Pushpendu Kar

doi:10.1109/access.2021.3131198

Saeid Pourroostaei Ardakani, Qianyu Liu + Show 3 more

Open Access

https://doi.org/10.1109/access.2021.3131198

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 10	License type: CC BY 4.0

Affiliation: University of Nottingham Ningbo China

Abstract

VANET routing aims to interconnect vehicular nodes via wireless links to transmit network packets. The goal of VANET routing is to reduce the communication cost, decrease the latency and increase the interoperability of the network. In this paper, a cluster-based routing protocol for VANETs called CNN is proposed. It takes advantage of the Hamming distance technique to partition a vehicular network into information-centric clusters based on the mobility of vehicular nodes. However, the proposed approach uses a named data network technique to forward network transmissions according to a hybrid communication model of Dedicated Short Range Communication and Mobile Agent. The former focuses on reactive intra-cluster link establishment, while the latter proactively forwards inter-cluster transmissions. A simulation measures the performance of the proposed approach and compares the results with two well-known VANET routing protocols: AODV and A- STAR. According to the simulation results, CNN outperforms the benchmarks in average end-to-end delay, path length, data delivery ratio, and total transmitted traffic, especially when the network is dense and the nodes are highly mobile.

Highlights

Recent advances in Vehicular Ad-hoc Network (VANET) address several new applications such as autonomous vehicle navigation, traffic monitoring, and fleet management [1]
CNN ROUTING we present a CNN routing approach in which network packets are classified into two classes: Data Interest Query (DIQ) and Data Interest Reply (DIR)
A- STAR was developed for VANETs in urban areas with M-grids, while Ad-hoc On-demand Distance Vector (AODV) was implemented in the real world and has a verified implementation model on OMNET++ (INET) [40]

Summary

Introduction

Autonomous driving vehicles (e.g., driverless cars or AGVs) collect and analyze ambient data such as environmental conditions, traffic status, road safety, and location information for navigation. They may need to communicate and share their data to avoid accidents, obstacles, and traffic, leading to increased public safety. VANET is similar to mobile Ad-hoc networks (MANETs) in terms of routing, as both are self-configuring [3] and [4]. (5) VANET changes network topology comparatively faster than MANET due to fast mobility. (6) VANET increases the transmitted network traffic compared to MANET due to frequent topology changes. The mobility of MANET is usually random. (4) VANET nodes usually do not suffer from energy constraints as they are continuously powered, whereas MANET nodes suffer from high energy constraints. (5) VANET changes network topology comparatively faster than MANET due to fast mobility. (6) VANET increases the transmitted network traffic compared to MANET due to frequent topology changes. (7) VANETs are large, dense and unbounded while MANETs are usually limited and sparsely distributed

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Conclusion