# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 3 Related Work  
[PERSON] et al. [23] designed a novel routing algorithm in addition to AODV to secure a network from BHA. Using this strategy, the malicious nodes are identified based on the node's behavior. If any are detected that node is deleted from the route. The experiments also show better Packet Delivery Rate (PDR) with reduced delay. [PERSON] and [PERSON] [24] presented an acknowledgment-based routing approach by which the communicating nodes send acknowledgment whenever the nodes receive the data packet. The algorithm suffers from high routing overhead as each node sends an acknowledgment message to the prior node. In addition to the above problem, [PERSON] and [PERSON] [25] developed a routing mechanism of trust where the behavior of nodes is analyzed based on the dropping rate of packets, but this protocol also suffers from high overhead because of the additional use of control packets. [PERSON] and [PERSON] [26] used the approach of mitigating a Gray Hole Attack (GHA) that takes the help of other nearby nodes, known as the nodes of the Intrusion Detection System (IDS), to monitor the performance of other communicating nodes. In the appearance of any malicious node, the packet drop value of the node is higher. In this case, the important message (\"ALERT\") is transferred among the networks to intimate other nodes to separate attacker nodes. As the algorithm works on the defined threshold, proper positioning of special nodes is required. [PERSON] and [PERSON] [14] designed a new approach source node that imitates the destination node of the total amount of packets transmitted from all expected routes. Query request is transmitted by the destination node, particularly in the case where the node cannot obtain the desired packets. In response to this query reply, a message is sent back to the node that is about two-hop counts in contrast to the destination node. Once the message of query reply is received, the destination node compares its prior-received data with the recently received data. In case an error appears, consider that node as the suspected node and add it to the list of malicious nodes. [PERSON] and [PERSON] [27] presented a combined trust-based bee approach to secure the network against BHA. ABC is used for the detection of a secure route. A new solution is generated based on the fitness function of bees. The designed algorithm shows enhancement in the PDR and end-to-end delay. [PERSON] and [PERSON] [28] presented a new trust-based approach that works on energy-aware routing for MANET. The BHA has been detected for single as well as for multiple routes formed during the data communication process. [PERSON] et al. [29] presented a mechanism in which the source node transmits the route response data packet after processing the node's information, which is later used for BHA detection. Whether the node is genuine or malicious is decided by the intermediate node. On the other hand, [PERSON] et al. [30] used a timer and baiting-based method for BHA detection in MANET. [PERSON] et al. [31] used a deep learning model for traffic flow prediction based on attention for inventory automation using a Wireless Sensor Network.