Advancements in wireless communication technolo-gies provide information interaction among vehicles, humans, and roadside infrastructure. As a result, the size and volume of content that must be streamed in real-time have grown rapidly, resulting in congested data traffic at content servers and a degradation in the user experience. Hence, to address these issues, an efficient Sliding Exponential Window-Kalman Filter (SEW-KF) and Weight Normalized ReLU-Memristor-like Activation Function-Long Short Term Memory (WNormRMAF-LSTM) based proactive content caching framework is proposed using Asynchronous Federated Learning (AFL). Primarily, the number of vehicles on the road is initialized and clustered using the Taylor Kernelized-Affinity Propagation Clustering (TK-APC) technique. Then, the cache vehicle is selected using HenonChebyshev Cosine-Honey Badger Optimization (HMCo-HBO). Next, the historical data (global model) from the AFL is given to cluster members by Cache Vehicle (CV) and trained using WNormRMAF -LSTM. After training the global model, each cluster member transfers the trained model to a CV, which in turn transfers to Road Side Unit (RSU). In RSU, content popularity prediction takes place using SEW-KF. After popularity prediction, more and less popular content is placed in Macro Base Station (MBS), whereas the popular content is placed in the RSU using Greedy Algorithm (GA). At last, data accessing takes place, where the availability of the data is checked and forwarded to the user. Finally, the results of the proposed method are compared to those of traditional algorithms.

Vehicular networks with Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V21) communications can benefit from Mobile Edge Computing (MEC) while sharing the computation abilities amongst vehicles. In this paper, we proposed an effective vehicle scheduling centered on Cumulative Distribution-Satin Bowerbird Optimization (CD-SBO) along with Xavier- Convolutional Neural Network (X-CNN) that leverages the vehicles' cache memory. In the proposed model, initially, regarding the data at-tained from the roadside unit (RSU), the range of vehicles on the road is initialized. After that, the vehicles are clustered using the Kullback-Leibler Divergence-K-Means Algorithm (KLD-KMA) model, which assigns RSU as a cluster of centroids. Then, to avoid data collision and uneven delay, the vehicles' request is balanced by using the CD-SBO algorithm. Next, the optimized requests are transferred to the cloud server through the gateway and Software Defined Network (SDN). Further, in the cloud server, data is decomposed into smaller components to enhance the cache storage. Then, the decomposed data is fed into the X-CNN classifier, which extracts the features from the Decomposed data and schedules the vehicles based on the extracted feature vectors. Finally, the results are forwarded to the targeted vehicles efficiently. The performance of the proposed method is better enhanced compared to the existing approaches.

With the emerging communication and computation technologies, the Internet of Vehicles (IoV) has become a new paradigm that enables vehicles to communicate with roadside units (RSUs) as well as with other vehicles to collect or exchange information. However, the inherent characteristics of IoV, such as the high mobility of the vehicle and the limited storage capacity of the edge nodes, cause numerous difficulties in developing a caching scheme. Therefore, in this article, a novel improved multinomial recurrent neural network (MRNN) classifier and Caesar combined key-based elliptic curve cryptography (2CK-ECC) algorithm are proposed to predict the vehicular mobility, security, and content caching for the IoV. Initially, the vehicles are checked for registration. Afterward, vehicle login and vehicle authentication take place. Then, mobility prediction is carried out for the authenticated vehicles using the MRNN Classifier. After that, vehicle clustering is done via the novel variance-balanced iterative reducing and clustering using hierarchies (VBIRCHs) technique. Next, the relay vehicles (RVs) are selected using the novel Harris self-avoiding hawks optimization (HSAHO). Furthermore, the data contents are divided into chunks and saved in the cache memory. From the cache memory, data is transferred to the RSU. Finally, a novel 2CK-ECC algorithm is investigated for secure data transmission. The experimental outcomes demonstrate that the proposed technique outperforms the existing baseline approaches.

Unmanned Aerial Vehicles (UAV) network has been explored widely in recent years. The deployment of small to medium-scale UAVs are being considered for several real-time applications such as monitoring, surveillance, search and rescue services. Throughput in the communication model plays a vital role in improvising available capacity and balancing the data traffic loads. Often UAVs carry real-time sensitive data which are vulnerable to cyber-attacks and the UAVs’ wireless communication networks become more vulnerable to various forms of cyber-attacks. In this paper, we propose a secure machine learning-based approach to maximize the throughput of a Software Defined Network (SDN) controller for better UAV communications and security. The proposed approach consist of three steps, a) optimal placement and user association of UAVs using Genetic algorithm b) SDN controller placement using shortest path via single connected graph and c) creation and detection of DDoS attack using Feedforward neural network classifier. The best position of UAVs as base stations is obtained from the energy’s estimation of previous base station and users. After UAVs optimal placement, the availability of network capacity under SDN controller is done by finding the shortest path using bellman ford algorithm. It intends to balance the loads and generate traffic-free data transmission. Additionally, detection of DDoS attacks is also studied. The combination of two feature reduction techniques, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are employed to find the features relevant to DDoS attacks. These features are then fed into the Feedforward neural network classifier that classifies the normal and abnormal network traffic data of UAVs. The proposed approach is evaluated using simulations. Experimental results show high efficiency and security measures in SDN-based UAV networks.

Internet of Vehicles (IoV) in 5G is regarded as a backbone for intelligent transportation system in smart city, where vehicles are expected to communicate with drivers, with road-side wireless infrastructure, with other vehicles, with traffic signals and different city infrastructure using vehicle-to-vehicle (V2V) and/or vehicle-to-infrastructure (V2I) communications. In IoV, the network topology changes based on drivers' destination, intent or vehicles' movements and road structure on which the vehicles travel. In IoV, vehicles are assumed to be equipped with computing devices to process data, storage devices to store data and communication devices to communicate with other vehicles or with roadside infrastructure (RSI). It is vital to authenticate data in IoV to make sure that legitimate data is being propagated in IoV. Thus, security stands as a vital factor in IoV. The existing literature contains some limitations for robust security in IoV such as high delay introduced by security algorithms, security without privacy, unreliable security and reduced overall communication efficiency. To address these issues, this paper proposes the Elliptic Curve Cryptography (ECC) based Ant Colony Optimization Ad hoc On-demand Distance Vector (ACO-AODV) routing protocol which avoids suspicious vehicles during message dissemination in IoV. Specifically, our proposed protocol comprises three components: i) certificate authority (CA) which maps vehicle's publicly available info such as number plates with cryptographic keys using ECC; ii) malicious vehicle (MV) detection algorithm which works based on trust level calculated using status message interactions; and iii) secure optimal path selection in an adaptive manner based on the intent of communications using ACO-AODV that avoids malicious vehicles. Experimental results illustrate that the proposed approach provides better results than the existing approaches.