# A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks
## 2 Background Survey
The SVM in the proposed approach is used to manage the data and create routing metrics to improve the routing performance. By applying a large amount of classified data (features including the distance between the forwarding node and the next-hop node, the moving direction, the acceleration, and the moving direction of the next-forwarding node), the model is obtained by training such a dataset in SVM. The simulation results show better reliability and communication efficiency are achieved. To estimate the required information for routing protocols, a unique routing information scheme known as the machine learning-assisted route selection (MARS) is proposed in [30]. Machine learning is utilized in MARS to keep track of road details in roadside units. MARS may also assist in determining the forwarding path among two RSUs according to the expected destination position and the approximate communication delays in both directions. To keep track of roads, we utilize RSUs and machine learning. MARS can forecast vehicle movement and choose appropriate routing paths with higher communication capacity for packet transmission. MARS can also assist in determining the forwarding direction between two RSUs. For VANET architecture, HQVR, a heuristic Q-learning-based routing algorithm [31], chooses a transitional hop based on the reliability of the connection. The learning protocol for HQVR is based on the data collected by transmitting beacon packets and is a distributed algorithm. The rate of beacon messages affects the convergence of the Q-learning algorithm, according to the authors, which makes convergence slower. The relation length ratio determines the learning rate in HQVR. The learning rate defines the sum of convergence according to the Q-learning procedure's functionality. As a result, the need for exploration decreases with a higher-quality link. As a result, the source can select the optimal path from among the several options. Whereas a reinforcement learning (RL) based routing protocol called RLRC [32] in VANET creates a cluster between the vehicles, the authors utilize an enhanced form of K-Harmonic Means (KHM). Since RLRC creates clusters to minimize the number of state spaces, the CH would be required to share a large number of packets with the CMs of their cluster. The graph-based deep learning model [33] in the communication network is discussed in various aspects, where the problems and Graph Neural Networks (GNN) based solutions are also listed. The construction method of wireless communication graph for different wireless networks and to introduce of the progress of various classical paradigms of GNNs are discussed [34]. GNNs-based deep reinforcement learning (DRL) architecture [35] can generalize the unseen network topologies used for training. To fully utilize the network resources deep graph reinforcement learning (DGRL) method [36] is effective, improving the data delivery rate and reducing the delay. As a result, when choosing a CH, RLRC counts the vehicle's energy parameter. The bandwidth parameter is chosen as the second parameter for selecting the CH to ensure smooth connectivity. The least distant node is chosen as the CH according to the relative distance. The SARSA model is used to optimize the RLRC procedure's routing mechanism, which reduces learning time. RLRC decreases the amount of state space and speeds up convergence by creating clusters. Table 1 demonstrates the comparative analysis of surveyed protocols in VANETs.