# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 1 Introduction  
Intelligent transportation systems (ITS) represent a significant breakthrough in smart city transportation, consisting of various types of communications between vehicles and other devices. Vehicular ad hoc network (VANET) is an adopted shape of mobile ad hoc networks (MANETs), where intelligent vehicles are substitutes for ordinary nodes. The vehicles in VANETs are embedded with onboard units (OBUs), which enable them to communicate with each other, i.e., vehicle-to-vehicle (V2V) communications, and/or with stationary roadside units (RSUs), i.e., vehicle-to-infrastructure communications (V2I) [1]. VANET is one of the most important innovations for enhancing the performance and safety of modern transport systems such as minimizing traffic jams in highly congested areas, avoiding traffic accidents, and disseminating congestion information with nearby vehicles. VANET applications allow vehicles to obtain news, traffic information, and weather updates in real-time through Internet access. Additionally, it avails entertainment such as gaming and file sharing through the Internet or local ad hoc networks to the passengers. Many unique characteristics, such as high mobility, predictable and restricted mobility patterns, rapid topological variation, and continuous battery charging, make VANETs different from MANETs. From the above properties, it is noticed that energy consumption is not a big problem in VANETs [2]. The highly evolving structure of VANET is a big challenge for the practical implications and commercialization of vehicular communication [3]. To deal with the above challenges, an effective routing protocol is necessary for V2V and V2I communications. In the absence of a reliable and stable routing protocol, advanced features of VANETs cannot be retained inreal-life [4]. The literature has enormous routing schemes to establish vehicle-to-everything (V2X) communications [2; 5; 6]. Generally, the existing routing schemes are classified into a couple of classes based on their structure and route discovering process [3; 6]. However, each class has specific limitations in various contexts [7]. Specifically, the non-hierarchical or linear routing schemes' performances degrade in highly dense networks in terms of computational and communication cost [3; 8; 9]. Thus, cluster-based routing protocols are introduced to optimally manage vehicles into groups based on their similarity, aiming to reduce the computational overhead by selecting an optimal number of clusters [4; 6; 7; 8]. Different protocols use various parameters for the selection of cluster members and cluster head (CH) in the literature, such as ID, degree of neighbors connectivity, propagation delay, velocity, travel time, average relative velocity, average link lifetime, route confidence level, number of vehicles to follow, link reliability, etc. [3; 6; 10]. However, the consistency and lifetime of cluster members and CHs are still challenging problems for researchers [1; 3]. In light of the above challenges, the primary objective of this research is to address the dynamic structure of VANETS using an optimal number of clusters. The link reliability metric refers to the probability that a particular link between two vehicles will be available for a specific time period \(t\)[3]. The reliability of a link considers the transmission range and inter-vehicle distance while ignoring the effect of spatial density. In contrast, link connectivity is another metric, showing that two vehicles will be connected if and only if they are within the same transmission range [11]. The connectivity metric considers the vehicle density along with the transmission range of vehicles. Thus, the connectivity is an essential metric for cluster formation and CH selection. Considering the definition of link connectivity, in this paper, our contribution is a connectivity-based clustering scheme, where the formation of clusters as well as the selection of CH are performed with the help of link connectivity.