Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid approach that incorporates the features of two state-of-the-art object detection models: You Only Look Once (YOLO) and Faster Region CNN (Faster R-CNN). The proposed hybrid approach combines the detection and boundary box selection capabilities of YOLO with the region of interest (RoI) pooling from Faster R-CNN, resulting in improved segmentation and classification accuracy. Furthermore, we skip the Region Proposal Network (RPN) from the Faster R-CNN architecture to optimize processing time. The hybrid model is trained on a local dataset of 10,000 labeled traffic images collected during driving scenarios, further enhancing its accuracy. The results demonstrate that our proposed hybrid approach outperforms existing state-of-the-art models, providing both high accuracy and practical real-time object detection for autonomous vehicles. It is observed that the proposed hybrid model achieves a significant increase in accuracy, with improvements ranging from 5 to 7 percent compared to the standalone YOLO models. The findings of this research have practical implications for the integration of AI technologies in autonomous driving systems.

Public cloud computing provides a variety of services to consumers via high-speed internet. The consumer can access these services anytime and anywhere on a balanced service cost. Many traditional authentication protocols are proposed to secure public cloud computing. However, the rapid development of high-speed internet and organizations’ race to develop quantum computers is a nightmare for existing authentication schemes. These traditional authentication protocols are based on factorization or discrete logarithm problems. As a result, traditional authentication protocols are vulnerable in the quantum computing era. Therefore, in this article, we have proposed an authentication protocol based on the lattice technique for public cloud computing to resist quantum attacks and prevent all known traditional security attacks. The proposed lattice-based authentication protocol is provably secure under the Real-Or-Random (ROR) model. At the same time, the result obtained during the experiments proved that our protocol is lightweight compared to the existing lattice-based authentication protocols, as listed in the performance analysis section. The comparative analysis shows that the protocol is suitable for practical implementation in a quantum-based environment.

Named Data Networking (NDN), an extension of the Content-centric network (CCN), is not an unfamiliar word. It is considered as one of the promising future network architecture. NDN focuses on naming the content regardless of end-to-end device addresses. NDN has been adopted for vehicular ad hoc networks (VANETs), and it has promising results. Though NDN has robust architecture, many challenges still exist, including consumer and producer mobility, naming, and Interest and data packet flooding. In vehicular NDN, the consumer vehicle broadcasts an Interest packet for the required content. The producer vehicle or an intermediate vehicle that contains the required content in its cache replies with the data packet after receiving the Interest packet. The data packet reaches the consumer vehicle via simple broadcasting. This data broadcast creates congestion in the network and needs to be mitigated. In this work, we coin a new term called name centrality, and using it along with received signal strength, we have proposed a novel scheme to control data broadcast storm in vehicular NDN. The scheme uses the mentioned two parameters to allow only a subset of potential forwarding vehicles to rebroadcast the data packet instead of all the vehicles which receive the data packets. This reduces the copies of data packets processed and propagated in the network and hence alleviates congestion. The scheme is evaluated through extensive simulation under different scenarios, and it outperforms the legacy schemes. The comparison has been made in terms of copies of total data packets processed, Interest satisfaction delay, and Interest satisfaction rate. Compared to other approaches, the proposed scheme reduced the average copies of data packets processed by 55% and 20% with varying Interest packet rates and 58% and 19% with varying vehicle speed.

Information-centric networking (ICN) is one of the promising solutions that cater to the challenges of IP-based networking. ICN shifts the IP-based access model to a data-centric model. Named Data Networking (NDN) is a flexible ICN architecture, which is based on content distribution considering data as the core entity rather than IP-based hosts. User-generated mobile contents for real-time multimedia communication such as Internet telephony are very common these days and are increasing both in quality and quantity. In NDN, producer mobility is one of the challenging problems to support uninterrupted real-time multimedia communication and needs to be resolved for the adoption of NDN as future Internet architecture. We assert that mobile node’s future location prediction can aid in designing efficient anchor-less mobility management techniques. In this article, we show how location prediction techniques can be used to provide an anchor-less mobility management solution in order to ensure seamless handover of the producer during real-time multimedia communication. The results indicate that with a low level of location prediction accuracy, our proposed methodology still profoundly reduces the total handover latency and round trip time without creating network overhead.

Vehicular Named Data Networking (VNDN) is a new architectural paradigm that combines Named Data Networking (NDN) with Vehicular Ad Hoc Networks (VANETs). VNDN enables vehicles to request and receive named content in vehicle network environment. However, VNDN faces a major challenge of robust data forwarding due to the high mobility of vehicles, which causes frequent network disruptions and packet losses. To overcome this challenge, we propose a real-time vehicle tracking and mobility prediction scheme based on the Recursive Least Squares (RLS) estimation technique. The RLS model uses multiple parameters, such as received signal strength (RSS), GPS coordinates, and speed, to dynamically estimate the locations of vehicles in real-time. Roadside Units (RSU) use tracking information to predict the moving vehicle and forward data packets toward the target RSU based on its trajectories. Additionally, we employ a hop-by-hop feedback Congestion Control (CC) mechanism for VNDN environments to improve data forwarding efficiency in congested networks. We conduct extensive simulations to compare the performance of our proposed multi-parameter RLS-based scheme with existing schemes. The results show that our scheme improves the data delivery ratio by 20-35%, reduces the average content retrieval latency and control overhead by over 40%, and enhances the reliability of content delivery in highly dynamic VNDN environments. Our proposed scheme demonstrates the potential of RLS-based tracking for vehicular mobility prediction to enable robust data dissemination architectures and protocols for next generation vehicle networks.

Vehicular Named Data Networking (VNDN) is one of the potential and future networking architectures that allow Connected and Autonomous Vehicles (CAV) to exchange data by simply disseminating the content over the network. VNDN only supports a pull-based data forwarding model, where the content information is forwarded upon request. However, in critical situations, it is essential to design a push-based data forwarding model in order to broadcast the critical data packets without any requests. One of the challenges of push-based data forwarding in VNDN is the broadcasting effect, which occurs when every vehicle broadcasts critical information over the network. For instance, in emergency situations such as accidents, road hazards, and bad weather conditions, the producer generates a critical data packet and broadcasts it to all the nearby vehicles. Subsequently, all vehicles broadcast the same critical data packet to each other, which leads to a broadcast storm on the network. Therefore, this paper proposes a Fuzzy Logic-based Push Data Forwarding (FLPDF) scheme to mitigate the broadcast storm effect. The novelty of this paper is the suggestion and application of a fuzzy logic approach to mitigate the critical data broadcast storm effect in VNDN. In the proposed scheme, vehicles are grouped into clusters using the K-means clustering algorithm, and then Cluster Heads (CHs) are selected using a fuzzy logic approach. A CH is uniquely responsible for broadcasting the critical data packets to all other vehicles in a cluster. A Gateway (GW) has the role of forwarding the critical data packets to the nearest clusters via their GWs. The simulation results show that the proposed scheme outperforms the naive method in terms of transmitted data packets and efficiency. The proposed scheme generates five times fewer data packets and achieves six times higher efficiency than the naive scheme.