MCA-V2I: A Multi-hop Clustering Approach over Vehicle-to-Internet communication for improving VANETs performances

Highlights

• This multi-hop clustering model extends the coverage area of clusters, optimizes the control overhead and improves cluster stability.

• A Mobility Rate is calculated based on mobility metrics to satisfy the mobility characteristics of VANET.

• MCA-V2I reduces the rate of control messages used in traditional clustering algorithms.

• MCA-V2I strengthens clusters’ stability by electing a Slave CH in addition to CH.

Abstract

The Internet of Vehicles is a new Intelligent Transportation System paradigm and a promising solution to improve conventional Vehicular Ad-hoc NETworks (VANETs) performances. It has received a great deal of attention in recent years, from many researchers. For this reason, several control mechanisms have been proposed for these networks to confront their challenges, such as dynamic topology and the scalability problem due to the high mobility of vehicles and the high number of connected vehicles, respectively. As an important mechanism used in a VANET, clustering has significantly improved the performance in numerous applications. In this regard, the present work proposes a new Multi-hop Clustering Approach over Vehicle-to-Internet called MCA-V2I to improve VANETs’ performance. MCA-V2I is based on the reasonable assumption that a vehicle can connect to the Internet via a special infrastructure called a Road Side Unit Gateway. Once connected to the Internet, each vehicle can obtain and share the necessary information about its Multi-hop neighbors to perform the clustering process. This latter is performed using a Breadth-first search (BFS) algorithm for traversing a graph based on a Mobility Rate that is calculated according to mobility metrics. MCA-V2I strengthens clusters’ stability through the selection of a Slave Cluster Head in addition to the Master Cluster Head. We evaluate the performances of the proposed scheme using network simulator NS-2 and the VanetMobiSim integrated environment.

Introduction

The Internet of Vehicles (IoV) is an evolution of conventional VANET. It extends VANET’s scale, structure and applications. This evolution leads to the emergence of new interactions at the road level among vehicles, humans and infrastructure [1]. It is an important field of research to improve conventional VANETs and their performances. Researchers have proposed several protocols for different aims and applications, such as data dissemination and aggregation, network overhead minimization, road safety, traffic management and mainly routing schemes.

Compared with VANET, IoV has many specific advantages and characteristics, such as developing and extending the exploitation of the Intelligent Transportation System (ITS) in different fields of research and industry [2]. The first main advantage is the quick and easy access to the Internet. This allows sharing safety information between vehicles and providing useful information, such as the availability of hotels, parking’s location, gas stations and even drivers’ comfort applications. The second advantage is the ability to support a significant number of connected vehicles (scalability). As a third advantage, Cloud Computing (CC) technology can be integrated into the vehicular networks. This emergent technology allows applications, resources and data to be stored in remote stations and servers that represent the cloud, so that they can be used by clients with low capacity. The CC technology manages the large amount of data generated by the connected vehicles. Finally, the IoV expands basic types of VANET communications such as Vehicle-to-Vehicle (V2V) and Vehicle-to-Road Side Unit (V2R) to new types of communications, such as Vehicle-to-Internet (V2I), Vehicle-to-Person and Vehicle-to-Device.

The architecture of an IoV network is usually composed of two main parts: Access Network (AN) and Backbone Network (BN). AN includes two main components: On-Board Unit (OBU) and Road Side Unit (RSU). First, the OBU is a device installed on the vehicle using Wireless Access in Vehicular Environment (WAVE) technology to ensure connection safety and reliability. It consists of four modules: GPS module, internal acquisition module, Input$–$Output module and Vehicle-to-X (V2X) module (X can be a vehicle, person, RSU, infrastructure or Internet). Second, the RSU is a computing device installed on the roadside that provides communication services to vehicles [3]. On the other hand, BN includes three main components: Transportation Control Center (TCC), Cloud Center (CC) and Internet. First, the TCC takes charge of managing and controlling all the components of AN. Second, CC is a virtual center containing servers to store data, resources and applications to serve vehicles. Finally, the Internet is a global network providing a variety of services and information. Nowadays, numerous communication technologies can be considered in IoV for V2X connectivity. In this respect, Masini et al. [4] discuss in depth different wireless access technologies and highlight the advantages and the limitations of each technology, from IEEE 802.11p and its related standards to short-range Cellular-V2X, such as LTE-V2X standard and other complementary technologies, such as visible light communication (VLC) and millimeter-Waves, up to hybrid communication and 5G. Fig. 1 illustrates the architecture of an IoV network.

As an important control mechanism used in VANET, clustering has significantly improved the performances in numerous applications, such as data dissemination and aggregation, network overhead minimization, road safety, drivers’ comfort and routing schemes. According to Yang et al. [5], clustering is the technique of dividing the network into groups of nodes called clusters. Each cluster has a cluster head and the rest of the nodes in the cluster are called cluster members. Typically, the clustering process is divided into five main phases: neighborhood discovery, Cluster Head (CH) selection, announcement, affiliation and maintenance [6].

To form stable clusters in VANET, many researchers have proposed various clustering protocols. These protocols differ from each other based on the criteria used to choose the CHs and perform the clustering process. Several of these protocols are detailed and discussed in Section 2.

According to the available literature, most of the proposed clustering algorithms [7], [8], [9], [10], [11] are focused only on one-hop clustering, which only allows communication between a Cluster Member (CM) and its CH with one-hop distance at most. Consequently, the coverage area is very small, and many clusters are formed, which affects the network performance and increases the rate of overlapping between clusters. Moreover, because the VANET is a subclass of MANETs, several proposed protocols are derived from the MANET clustering schemes [12], [13]. However, these schemes do not consider the mobility characteristics, the dynamic topology and the limited driving directions of VANET; moreover, they do not consider energy problems [14]. Furthermore, most of the proposed clustering protocols do not use mechanisms that exploit the Internet and to take advantage of their large services to improve the performances of VANET. Several proposed mobility-based clustering approaches [15], [16], [17], [18] are based on the broadcast of control messages, which causes overloading of the networks and leads to many collisions, especially because the number of messages is high due to the multi-metric mechanism used.

In this work we propose a new Multi-hop Clustering Approach over Vehicle-to-Internet communication called MCA-V2I to improve VANETs’ performance. The main idea of this work is to perform a clustering algorithm using Internet access. MCA-V2I is based on the reasonable assumption that a vehicle can connect to the Internet via a special infrastructure called a Road Side Unit Gateway (RSU-G) to obtain and share the necessary information about its multi-hop neighbors to perform the clustering algorithm. It is performed using a Breadth-first search (BFS) algorithm for traversing the graph and based on a Mobility Rate (MR) that is calculated according to some mobility metrics such as node connectivity, average relative velocity, average distance and link stability. In MCA-V2I, a vehicle with low MR is suitable to be elected as the Master CH (MCH). Therefore, all the multi-hop neighbors of the new elected MCH become Cluster Members (CMs). The MCA-V2I scheme strengthens clusters’ stability through the election of a Slave Cluster Head (SCH). We evaluate the performances of MCA-V2I using network simulator NS-2 and the VanetMobiSim integrated environment.

The main contributions of this work are as follows.

• A new multi-hop clustering model is proposed. Compared with one-hop clustering schemes, this model is designed to extend the coverage area of clusters, reduce the number of clusters, optimize the control overhead and improve cluster stability.

• A Mobility Rate is introduced for the clustering algorithm. This parameter is calculated based on mobility metrics to satisfy the requirements of the new features of VANET, and to consider its mobility characteristics.

• MCA-V2I provides Internet access to vehicles to obtain and share the necessary information to perform the clustering algorithm. This benefit significantly reduces the rate of control messages used in traditional clustering algorithms. Therefore, MCA-V2I can significantly improve the network overhead.

• MCA-V2I strengthens clusters’ stability through the election of an SCH in addition to the MCH.

The rest of this paper is organized as follows. Section 2 presents related work. Section 3 describes the preliminaries of the proposed approach. Section 4 introduces the proposed approach in detail. Section 5 presents the experimental results. Finally, a conclusion is presented in Section 6.