# An Efficient Framework Model for Optimizing Routing Performance in VANETs
## 1 Introduction  
Vehicular Ad hoc Networks (VANETs) [1] are a collection of distributed mobile nodes that are self-configured, in which each node can be a router for multi-hop relay nodes. VANETs are designed to share most features of the Mobile Ad hoc networks (MANETs), which are applied based on IEEE 802.11p technology developed to support Intelligent Transportation Systems (ITS) applications [1; 2; 3]. These applications are based on a context of a fully connected traffic system enabled by VANETs, where vehicles have the possibility to communicate with each other over three types; Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), Device-to-Device (D2D) communications, and hybrid combination of V2V and V2I [4; 5]. ITS applications require a precise representation ability of routing protocols to meet the high demands of VANETs as well as QoS requirements. High movement makes the topology of Vehicular Ad-hoc Networks really short in live communication link. Further, the movement can also deteriorate routing protocols in terms of signal and message loss. These movements can negatively impact the routine of applications that have Quality of Service in terms of high responsibility, and low latency. Adjusting routing protocols for V2V communication network in order to satisfy various QoS requirements in applications was studied in [6; 7; 8]. However, the natural characteristics of the vehicular network are constrained by several factors such as limited link capacity, bandwidth, and transmission range coverage. These restrictions lead to a reduced routing efficiency and an increased routing overhead [1]. The objectives of most of the current routing protocols focus on either tuning the routing parameters or adapting the efficiency of route selection to reduce network overhead. Several routing protocols were presented to utilize network resources and enhance the routing efficiency in VANETs [9]. However, most of these protocols still have shortcomings when meeting the QoS requirements and in guaranteeing the stability of network topology during the routing process. Adopting suitable routing parameters or routing functionality to enforce different QoS achieving a high level of reliability is dependent mainly on network characteristics and their constraints. A high level of communication reliability is needed to enhance traffic prioritization and optimize network resources. Due to the high-level perspective, MANET is not suitable in VANETs, it needs to be developed to meet the specific characteristics of VANETs. Different methods to tune the routing parameters had previously been introduced to ensure a high level of performance in the dynamic network [10], yes most of them prove inadequate. Most of the them considered QoS as a major requirement to enhance the routing efficiency [11], overcomes a number of problems such as unpredictable node density [12] and the limitation of coverage [13; 14]. Moreover, to address characteristics of a highly dynamic scenarios, this study can be extended by several research areas such optimization-based multiple objective which enhance delivery via D2D Communications in term of energy efficiency [5], improving the transmission capacity in dense network [15], and in a heterogeneous mobility network where the delay or disruption network tolerance presented in [16]. Most of the recent studies considered QoS to optimize the routing efficiency [17] by introducing the optimization concept [18] and Particle optimization (PSO) technique [19] in order to reduce the impact of roads constraints on routing performance [20]. Tuning routing configuration parameters is one of the most common strategies used to keep track of neighboring node status [21]. A number of studies in the literature adjusted routing configurations making it suitable and easily applied into different kinds of network [22; 23]. In addition, studies tried to improve routing efficiency mainly in terms of network connectivity or available bandwidth by using multi-objective optimization strategy [22; 24].