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Top1. Introduction
The number of vehicles in metropolitan transport has increased enormously, causing a pressing need for deploying vehicular ad hoc networks (VANETs) (Gaber et al., 2018). Smart cities, infotainment, detection of travel paths, journey time forecast, and prevention of traffic accidents are the main applications of VANETs (Haider et al., 2019). Avoiding of traffic casualties is of utmost concern among such applications, as about 1.25 million people die in road accidents per year, while about 20-50 million people are injured or disabled (Haider et al., 2019), (Ullah et al., 2020). To evade such undesirable circumstances, clustering techniques are adopted in VANETs to disseminate EMs among vehicles (hereinafter nodes). In clustering, identical nodes are grouped and managed by a cluster head (CH) node. The role of clustering is to give sufficient reaction time for nodes to take preventive measures in case of an emergency. EMs are transmitted among CHs of clusters, and CHs further transmit the EMs to their member nodes. This helps in reducing transmission overhead by minimizing the broadcast overflows. A clustering technique can be built on either a vehicle to infrastructure (V2I) model or a vehicle to vehicle (V2V) model. Due to the cost of organizing and upholding road-side units (RSUs), V2I models have higher capital and working overhead (Haider et al., 2019), (Cooper et al., 2016).
However, most of the V2V clustering techniques presented thus far are designed for unidirectional road traffic in which the direction of nodes is ignored. Such clustering techniques fail to perform in two-ways scenarios where nodes move in opposite directions causing unstable clusters. A novel clustering technique, namely, probabilistic direction aware cooperative collision avoidance (P-DACCA), is presented in (Haider et al., 2020) for two-ways highways. Nevertheless, P-DACCA cannot cater to two-ways road traffic in metropolitan VANETs, which consist of intersections and roundabouts, allowing the mobility of nodes in various directions. This paper presents a robust clustering protocol, called bi- directional metropolitan routing protocol, for routing EMs in VANETs as an extension of P-DACCA. aims at creating dependable and stable clusters in two-ways metropolitan VANETs. We introduce an improved k-medoids algorithm that integrates Hamming distance function as an additional parameter to provide a delicate procedure to create stable clusters. After clustering, the next step is to select a center node as a CH. A median method is used to select a CH based on either even or odd number of nodes in a cluster. makes the following contributions:
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A novel routing protocol is presented to disseminate EMs in two-ways road traffic through stable and reliable clustering for metropolitan VANETs.
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A modified k-medoids algorithm is presented for clustering in two-ways road traffic.
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A median method is presented for selecting CHs to ensure the long-running of a cluster.
The remainder of this work is presented in the following sequence. Section 2 describes related work. Section 3 presents the proposed protocol, and Section 4 evaluates its performance. Finally, Section 5 concludes the paper with directions for future work. Table 1 presents a summary of the related work and Table 2 presents a list of notations used in this paper.