# A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks
## 2 Background Survey  
While going through the current literature, we reviewed that widespread research on routing protocols has been proposed in VANET. A cluster-based lifetime routing protocol called CBLTR [21] is proposed, aiming to maximize the stability of routes and average throughput in a bidirectional sector situation. The CHs are elected by considering the vehicle's lifetime as one of the parameters inside each cluster. The CHs select the optimal route according to its current location, destination location, and average throughput. The proposed protocol also minimizes the control overhead in the clusters among the cluster members and the CH. The simulation results reveal that it outperforms in terms of E2E delay and throughput. Although, QoSBeeVanet is proposed in [22], a QoS multi-path routing protocol. It is centered on a biological model of bee transmission in the quest for food sources. It utilized a scout and forager to find the network and transfer data to the destination. Every scout recorded its data in the routing table and assessed its quality using a weighting factor. The hybrid bee swarm routing (HyBR) approach for VANET was introduced in [23]. HyBR is a multicast and unicast routing that ensures road security by communicating packets with minimal latency and large data delivery. During high network density, it utilizes Scout and Forager for network findings which are motivated by bee communication. However, during less density, it utilizes a geography-based approach, which uses a GA to determine the shortest route between source and destination. A hybrid clustering mechanism is proposed in [24], which merges context- and geographic-based clustering methods. During clustering, every node calculates a weight based on specific parameters: velocity, distance, residual lifetime, point of interest, and direction. The node with the maximum weight is chosen as CH. The proposed research decreases the overhead in the network and the destination-aware inter-clustering routing, which improves the overall PDR and reduces the E2E delay. A hybrid, multipath ACO-based routing approach (MAZCORNET) is proposed in [25] to determine multiple paths among vehicular nodes. In MAZCORNET, the network is split into numerous zones, a proactive mechanism is utilized to determine a path within each zone. A reactive mechanism is utilized to determine a route among zones by utilizing the local data accumulated in each zone. This technique is scalable and fault-tolerant. CBQoS [26] is a new QoS-based unicast routing for VANET. It considers two procedures: a clustering approach that establishes and enhances the transmission of routing information to meet QoS necessities, and a routing information optimization algorithm, and an ABC algorithm that determines the optimal paths among source and destination using QoS parameters such as usable bandwidth, E2E delay, and connection expiration time. An improved HS optimization (EHSO) algorithm [27] considers the optimized link state routing (OLSR) parameters' design by storing two common selection techniques in memory: roulette wheel and tournament selection. The improved harmony search optimization (EHSO) outperforms the OLSR in terms of PDR and routing overheads, according to simulation findings applied to a highway scenario. A location-based geocast routing protocol [28] that uses PSO with a next-vehicle approach and a fitness feature that is built in such a way that it can quickly locate local and global maxima. The authors created a PSO with a fitness feature that maximizes the distribution ratio and minimizes delay, routing load, and packets drop when choosing an appropriate next-hop vehicle to send information to the geocast region on time. Since the fitness feature utilized in PSO minimizes delay and routing load, the proposed protocol performs better. The literature also incorporates numerous studies [29; 30; 31; 32] that have embraced machine learning methods to resolve the routing issue in VANET. A greedy forwarding routing algorithm [29] in VANET is based on the SVM technique.