Research Article
A Distributed Multi-hop Clustering Algorithm for Infrastructure-Less Vehicular Ad-Hoc Networks
@INPROCEEDINGS{10.1007/978-3-319-95153-9_7, author={Ahmed Alioua and Sidi-Mohammed Senouci and Samira Moussaoui and Esubalew Alemneh and Med-Ahmed-Amine Derradji and Fella Benaziza}, title={A Distributed Multi-hop Clustering Algorithm for Infrastructure-Less Vehicular Ad-Hoc Networks}, proceedings={Information and Communication Technology for Development for Africa. First International Conference, ICT4DA 2017, Bahir Dar, Ethiopia, September 25--27, 2017, Proceedings}, proceedings_a={ICT4DA}, year={2018}, month={7}, keywords={Infrastructure-less VANET Distributed multi-hop clustering Cluster stability}, doi={10.1007/978-3-319-95153-9_7} }- Ahmed Alioua
Sidi-Mohammed Senouci
Samira Moussaoui
Esubalew Alemneh
Med-Ahmed-Amine Derradji
Fella Benaziza
Year: 2018
A Distributed Multi-hop Clustering Algorithm for Infrastructure-Less Vehicular Ad-Hoc Networks
ICT4DA
Springer
DOI: 10.1007/978-3-319-95153-9_7
Abstract
Vehicular Ad-hoc Networks (VANETs) aim to improve travailing safety, comfort and efficiency via enabling communication between vehicles and between vehicles and infrastructure. Clustering is proposed as a promising technique to efficiently manage and deal with highly dynamic and dense features of vehicular topology. However, clustering generates a high number of control messages to manage and maintain the clustering structure. In this paper, we present our work that aims to facilitate the management of the disconnected infrastructure-less VANET areas by organizing the network topology using a distributed multi-hop clustering algorithm. The proposed algorithm is an enhanced version of the distributed version of LTE for V2X communications (LTE4V2X-D) [7] framework for the infrastructure-less VANET zone. We are able to improve the performance of LTE4V2X-D to better support clustering stability while decreasing clustering overhead. This is made possible due to a judicious choice of metrics for the selection of cluster heads and maintenance of clusters. Our algorithm uses a combination of three metrics, vehicle direction, velocity and position, in order to select a cluster-head that will have the longest lifetime in the cluster. The simulation comparison results of the proposed algorithm with LTE4V2X-D demonstrate the effectiveness of the novel enhanced clustering algorithm through the considerable improvement in the cluster stability and overhead.