## 2 Literature Review  
An algorithm developed by researcher [7] in a vehicular network was utilized to locate Black Hole nodes [8]. Each node in the network has a trust routing table (TRT), which comprises a list of all the network's trustworthy nodes. In response to packets received from the destination node, each sender node updates its trust value on a periodic basis [21; 22; 23; 24]. To determine which neighbor is the best, the trust value is combined with the progress value. For the purpose of finding a single Black Hole node, the author employs a greedy geographical routing protocol, also termed as position-based routing. Following the receipt of the initial RREQ, the authors of [9] enhanced performance on the original AODV by inserting a timer into the Rimer Expired Table after receiving the initial RREQ to collect requests from other nodes. The collect route replay table also saves the destination number and time, counts the timeout on the basis of the initial RREQ and assesses the validity of the route by checking the threshold values with the destination number and the received time [5]. The watchdog approach was proposed by [10], who offered a mechanism for identifying malicious nodes. In this approach, nearby nodes keep track of the data packets that they overhear being transmitted and received by their surrounding nodes and then store those records. When the number of failures exceeds a specific threshold, it is transmitted to the source node. It is not possible to detect rogue nodes using this method when collisions occur [10]. The researchers in [11] proposed methods for detecting and eliminating Black Hole attacks. The header of the AODV is changed in this manner by the addition of a parent vehicle. The addresses of the packet's previous originator are contained in this parent vehicle field. In order to eliminate route recovery sessions, which are also known as link failure sessions, the route redundancy technique is utilized. Although it decreases E2E latency, routing overhead increases as a result of this. In [25], the use of a hash function to defend against AODV's Black Hole attacks was advocated. Here, the first optimal path is rejected, and the second optimal path is chosen over the first optimal path. Here, the hash function is employed to ensure data integrity, and this function may be used to detect the rogue node in the second optimum path. The message's hash value is calculated at the source node as (SHA-ONE), and the received message's hash value is calculated separately at the destination node as (SHA-ONE) (SHA-TWO). It is possible that the message received will not be altered if both hash values are the same; otherwise, a data packet error (DPE) message will be sent throughout the network. A table will be used to keep the record of information, which is called the routing table. By combining the ad hoc On Demand Multipath Distance Vector (AOMDV) routing with soft encryption, the researchers in [8] provide a method for combining trust-based multipath AOMDV routing with a soft-encryption technique. This strategy uses XOR operations to encrypt messages, the second approach uses the multiple route node disjoint AOMDV routing protocol with various trust-based values, and the third approach uses XOR procedures to encrypt messages. The final stage is message decryption, which occurs at the destination end of the transmission. According [14], the 'Resource Efficient Accountability (REAct)' method includes an auditing, searching, and identifying procedure. A feedback message is delivered to the source node whenever a packet is lost, and the source node then picks an audit node. The audit node makes use of the bloom filter in order to obtain a proof of behavior. Although the routing overhead is reduced, the identification latency is enhanced with this method. There is a significant drawback to this technique in that it does not utilize cooperative Black Hole identification [14]. In [26] a heuristic technique to detect, in MANETs, Black Hole assaults.