Mobile Ad Hoc Networks (MANETs) are self-organizing, self-configuring, and infrastructure-less networks for performing multi-hop communication. The source mobile node can transmit the information to any other destinat...
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Mobile Ad Hoc Networks (MANETs) are self-organizing, self-configuring, and infrastructure-less networks for performing multi-hop communication. The source mobile node can transmit the information to any other destination node, but it has limitations with energy consumption and battery lifetime. Since it appeals to a huge environment, there is a probability of obstacle present. Thus, the network requires finding the obstacles to evade performance degradation and also enhance the routing efficiency. To achieve this, an obstacle-aware efficient routing using a heuristic-based deep learning model is proposed in this paper. Firstly, the nodes in the MANET are employed for initiating the transmission. Further, it is needed to be predicted whether the node is malicious or not. Consequently, the prediction for link connection between the nodes is achieved by the Optimized Bi-directional Long-Short Term Memory (OBi-LSTM), where the hyperparameters are tuned by the adaptivehorseherdoptimization (AHHO) algorithm. Secondly, once the links are secured from the obstacle, it is undergone for routing purpose. Routing is generally used to transmit data or packets from one place to another. To attain better routing, various objective constraints like delay, distance, path availability, transmission power, and several interferences are used for deriving a multi-objective function, in which the optimal path is obtained through the AHHO algorithm. Finally, the simulation results of the proposed model ensure to yield efficient multipath routing by accurately identifying the intruder present in the network. Thus, the proposed model aims to reduce the objectives like delay, distance, and power consumption.
Vehicular Ad-hoc Networks (VANET) help to increase traffic safety, manageability, and efficiency. VANET facilitates effective communication in the vehicular nodes. Here, the network attack is one of the significant co...
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Vehicular Ad-hoc Networks (VANET) help to increase traffic safety, manageability, and efficiency. VANET facilitates effective communication in the vehicular nodes. Here, the network attack is one of the significant consequences of VANET, which may affect network security. The high mobility of the nodes may become difficult to transfer the data through the vehicle. In this article, a novel method of Elliptic Curve Cryptography (ECC) is proposed. The key is generated by Key Generation Unit (KGU), and also the key is fed into the Road Side Unit (RSU). The keys in Enhanced ECC (EECC) are optimized using the adaptive horse herd optimization algorithm (AHOA). During decryption, the key of source node K1 is to be taken from RSU, where the key is processed by the Digital Signature method to obtain EECC key K2. With the generated K2, the destination node can retrieve the data by the proposed model. Thus, the simulation analysis of the proposed AHOA-EECC method has attained less time complexity which was compared against the high value of 5%, 12.4%, 50%, and 13.7% for GWO-EECC, PSO-EECC, AOA-EECC, and HOA-EECC. Finally, the outcome of the proposed system enhances the security level and performs better data transmission.
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