## 2 Literature Review
### Effectual Routing Strategies
[32] introduced a modified TAD-HOC routing mechanism featuring a discerning forwarding strategy. In this approach, the utilization of AODV is complemented by group authentication keys to ensure the secure and expeditious delivery of packets. Inthe framework we propose, TROPHY messages are meticulously received in sequence exclusively by nodes that have been duly authenticated. Four distinct entities synergize within our proposed approach to establish a fortified network environment: onboard units, roadside units, key distribution centers (KDC), and human operators. The KDC serves as the keystone, generating cryptographic keys regularly and disseminating them to network nodes for robust authentication. If a node is not actively engaged in the routing process, the KDC and RSUs proactively refresh and renew the routing infrastructure. This entails meticulously removing redundant or malevolent nodes and disseminating pertinent information regarding the identified malicious nodes to all other network constituents. A judicious forwarding algorithm is harnessed to detect extra packets, and once redundant packets are identified, the messages are transmitted in a covetous fashion. Our protocol exhibits a remarkable 92% efficiency gain in comparison to TAD-HOC. The authors in [33] discuss a system for trust-based secure routing in vehicular ad hoc networks (VANETs) using deep learning-based attack detection. Here is an analysis of the reference's advantages and disadvantages compared to your work and a hypothetical comparison of results. The first advantage is the hybrid approach that combines an ANN model for attack detection with a modified AODV protocol for routing. This combination can provide a comprehensive solution for VANET security and routing. Detection of black hole attacks addresses the problem of black hole attacks, a significant security concern in VANETs. Using an ANN model, the system claims to achieve a high accuracy of 98.97% in detecting black hole attacks, a crucial advantage in securing data transmission. Cluster-based routing employs cluster-based routing, enhancing network organization and reducing routing complexity in VANETs. Performance evaluation mentions a thorough evaluation of the system's performance using various metrics such as accuracy, precision, recall, F1 score, and loss. It also considers network parameters like end-to-end delay, network throughput, and hop count, comprehensively assessing routing efficiency. It outperforms existing techniques, demonstrating its potential as an improved VANET security and routing solution. Disadvantages include lack of implementation details: the reference does not provide detailed information on the implementation of the proposed system, making it challenging to replicate the work or assess its real-world feasibility. Limited information on the ANN model: while the reference mentions the use of an ANN model for attack detection, it lacks information about the architecture and training process of the model. This omission can be a drawback for those interested in replicating or improving upon this aspect of the work. Evaluation using a specific dataset states that it uses the \"BHT dataset\" for evaluation, but it does not provide details about this dataset. The suitability and representativeness of the dataset can be necessary for the credibility of the results. To compare your work with the proposed system, compare the methodologies of your work and the reference. Assess whether the hybrid approach and ANN-based attack detection are similar or different from your approach. Results show that if you have conducted experiments and evaluations in your work, compare your results with those presented in the reference. Look for differences in performance metrics, such as accuracy, precision, recall, F1 score, and routing efficiency. Implementation evaluates the practical implementation aspects of your work compared to the reference. Consider the ease of implementing your solution in a natural VANET environment.