# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 2 Related Works  
In Ref. [11] a new approach was presented to address the drawbacks in VANETs, such as lack of energy efficiency, delays, security issues, and bandwidth problems. So as so to improve the performance of these parameters, optimal RSU placement was proposed, centered on the Memetic-based RSU (M-RSU) placement algorithm. With the help of this algorithm network communication delay was reduced and communication delay increased which led to improvement in the efficiency of the VANETs. Additionally, a Distributed ML (DML)-based Intrusion Detection System (IDS) was used to improve the overall performance of the network. However, the network produced moderate throughput. In Ref. [12] a novel approach to improve the packet delivery ratio (PDR) and reduce the delay of VANETs was presented, which used Efficient Clustering V2V Routing Based on Particle Swarm Optimization (PSO). The results packet delivery ratio (PDR) improved by 20% and the delay was reduced by up to 47%. The network, however, failed to achieve high throughput and efficiency. In Ref. [13] a design to improve efficiency and flexibility, called Novel Routing and Hybrid based Clustering Scheme (NRHCS) for VANETs, was introduced, based on cluster formation and cluster head election re-clustering. In the end, it achieved high efficiency, but, due to various activities, the overhead and delay were high. In Ref. [14] a promising technique was developed, that was based on a clustering approach, to improve the performance of vehicular communication, called the Efficient Cluster Head Selection (ECHS) scheme. This method produced a high lifetime, low packet loss and delay, and low overheads. However, it failed to achieve high throughput, which meant it was not suitable for huge dynamically-varying networks. In Ref. [15], to improve the overall performance of VANETs, an efficient communication pattern for VANETs was suggested, which was based on a Meth-Flame Optimization (MFO) scheme-based clustering approach called AMONET. Here bio-inspired algorithms were used for the process of optimized cluster head selection, which made the system more reliable and efficient in communication. The overall performance achieved by the network was good. However, it might create more overhead when applied to a network with a huge number of dynamic vehicles. In Ref. [16] a cluster-based model was proposed to improve the efficiency, scalability and lifetime of VANETs, called a fuzzy logic-based clustering control scheme. It is a context-aware scheme with multi-criteria decision making, mainly fuzzy, used for the rank calculation of multi-attribute candidates to select the CH in the optimal way. This method reduced the energy consumption in the network which led to improving the network efficiency and lifetime. However, the method increased the routing overhead which led to an increase in delay in communication. In Ref. [17] a clustering-based Vehicular Ad-hoc Network (VANET), in which a dual-slot transmission scheme for intra-cluster interference suppression with a graph-based algorithm was proposed. In general, this method produced moderate results with respect to delay and energy. On the other hand, the packet delivery ratio achieved by this approach was low. In Ref. [18] a Multi-channel Clustering-based Congestion Control algorithm to reduce congestion in the network was proposed. It is an infrastructure-independent clustering method to mitigate network congestion. Here, the overall performance improved by reducing the power consumption where control information overhead was reduced. However, this method failed to achieve high throughput and delivery ratio. In Ref. [19] a method to reduce delay, called Traffic Differentiated Clustering Routing (TDCR) mechanism, was introduced. The core idea of this method is a centralized one-hop clustering approach with a data delivery optimization method. From the simulation evaluation the proposed work achieved low delay and overhead. However, the throughput and bandwidth utility was poor. In Ref.