This research introduces the Multi-constraint Routing Mechanism using Hybridization (MRMH). The proposed work innovatively combines the strengths of Grey Wolf Optimization (GWO) and Sequential Quadratic Programming (SQP). While GWO is adept at global search, its tendency for premature convergence is effectively countered by SQP's excellence in nonlinear constraint management and local optimization. MRMH further benefits from a novel weighted distance approach and a nonlinear decay formulation, enhancing the balance between exploration and exploitation phases in VANET optimization. Our fitness function, designed around essential VANET metrics like inter-cluster distance and vehicle orientation, ensures the applicability of MRMH in real-world scenarios. 

This research introduces the Meta-Enhanced Recurrent Multi-Agent Reinforcement Learning (M-RMARL) framework, designed to tackle the challenges of reliable routing and dynamic spectrum management in Cognitive Vehicular Ad Hoc Networks (CR-VANETs). The framework is built on Meta-Agnostic Meta-Learning (MAML), utilizing Meta-Learned Deep Recurrent Q-Networks (DRQNs) to significantly reduce training time, enabling vehicles to quickly identify optimal routes and enhance spectrum sensing with minimal adjustments. M-RMARL also features a dynamic spectrum management system that employs Long Short-Term Memory (LSTM)-based meta-predictive models to forecast future spectrum availability and network conditions. These predictions allow DRQNs to make proactive, intelligent decisions, improving spectrum efficiency. To ensure secure communication, the framework incorporates a Trust-Based Meta-Coordination mechanism, which dynamically evaluates agent trustworthiness and integrates these assessments into the decision-making process. Additionally, the framework leverages a Hierarchical Meta-Agent Coordination architecture, where Roadside Units (RSUs) manage global coordination and meta-learning updates, while vehicle agents implement the derived policies. 

Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements such as location, time, velocity, inter-dependencies, and distance. An integrated attention mechanism refines these matrices, ensuring precision in capturing vehicular dynamics. The culmination of these components results in routing decisions that are both responsive and anticipatory. 

This paper proposes a software-defined vehicular fog computing (SDFC) framework that refines resource allocation in VANETs. Our SDFC framework utilizes an intelligent controller placement that strategically positions decision-making entities within the network to optimize data flow and resource distribution. This placement is governed by a dynamic clustering algorithm that responds to variable network conditions, an advancement over the static mappings used by traditional methods. By incorporating parallel processing principles, the framework ensures that computational tasks are distributed effectively across network nodes, reducing bottlenecks and enhancing overall network agility. Empirical evaluations (testbed) and simulation results of our framework indicate a substantial increase in network efficiency: a 28% improvement in average response time, a 23% decrease in network latency, and a 25% faster convergence to optimal resource distribution compared to state-of-the-art methods.

This research introduce an enhanced verification model, DyBatch: Message Prioritization and Dynamic Batch Verification in Large-scale IoV Networks, designed for Edge-computing enabled IoV (e-IoV) networks. DyBatch effectively tackles message loss by classifying Basic Alert Messages (BAMs) based on urgency through a flag-based mechanism, coupled with a flexible batch size concept to overcome fixed-size limitations. The model employs Verification-Proxy Vehicles (VPVs), special vehicles that handle verification during Edge Node (EN) bottlenecks. DyBatch aims to minimize message loss and verification delays, enhancing real-time application suitability.

This research proposes RF -CVN, a recurrent reinforcement learning (RRL) technique to sense the spectrum and discover a trustworthy path between the source and the destination using belief transmission (i.e., channel conditions, interference levels, and vehicle locations). We first devise a deep recurrent Q network for a multi-channel access scheme for unlicensed users to use available channels. The RRL allows the Q function to learn hidden states in partial observation or highly time-correlated network sensing cases. Later, the trust values are used to gain a more nuanced understanding of the network state, thereby enhancing the efficiency and reliability of the routing process. In this work, we argue that trust should be an integral part of the routing process and, therefore, design a trust mechanism to select a path. The trust mechanism aims to detect those spectrums that over-utilize or under-utilize their channel capacity during the local training.

This research propose a novel framework, qIoV that integrates quantum computing with the Internet-of-Vehicles (IoV) to leverage the computational efficiency, parallelism, and entanglement properties of quantum mechanics. Our approach involves the use of environmental sensors mounted on vehicles for precise air quality assessment. These sensors are designed to be highly sensitive and accurate, leveraging the principles of quantum mechanics to detect and measure environmental parameters. A salient feature of our proposal is the Quantum Mesh Network Fabric (QMF), a system designed to dynamically adjust the quantum network topology in accordance with vehicular movements. This capability is critical to maintaining the integrity of quantum states against environmental and vehicular disturbances, thereby ensuring reliable data transmission and processing. Moreover, our methodology is further augmented by the incorporation of a variational quantum classifier (VQC) with advanced quantum entanglement techniques. This integration offers a significant reduction in latency for hazard alert transmission, thus enabling expedited communication of crucial data to emergency response teams and the public.

A comprehensive assessment of Vehicular Ad-hoc Networks reveals suboptimal efficiencies at the data layer, specifically regarding default broadcast intervals. Such inefficiencies lead to escalated packet collisions and subpar utilization of the delay time counter—factors that undermine the synergistic interplay between Active Safety Systems (ASS), such as Adaptive Cruise Control (ACC), and their passive safety counterparts. To address these intricacies, this research proposes an innovative mathematical framework tailored for the IEEE 802.11p MAC layer. We propose a model that elucidates the intricate dynamics of the delay time counter and offers refined broadcast intervals buttressed by robust algorithmic strategies.

This research proposes a novel queue length-based stochastic task migration strategy that leverages model predictive control (MPC) and Lyapunov optimization techniques. Our approach employs the queue length at the edge node as the criterion for offloading decisions. The MPC controller dynamically allocates the processing power and bandwidth resources to vehicles based on their current requirements, facilitating prompt offloading decisions. The Lyapunov optimization ensures long-term system stability. Our method also incorporates dynamic request selection from multi-dimensional queuing load optimization and ensures fair and efficient load distribution, thereby enhancing edge server utilization.

This study provides a novel VANET routing protocol known as SDCast, based on ad hoc on-demand distance vector routing that utilizes an SDN-based Q-learning algorithm and considers various constraints (link availability duration, link latency, and bandwidth). The protocol maintains vehicle coordination by employing a clustering architecture that considers the vehicles’ relative motions and velocities. Furthermore, SDCast finds the best path by combining a probability distribution function with a global search approach. 

This paper investigates the feasibility of cache content prediction and coherence in the context of secure communication and search. We introduce a distributed multi-tier mobility-assisted edge intelligence based caching framework for the Internet of Vehicles (IoVs), called CacheIn. The proposed framework leverages user preferences, data correlations, and mobility information to prefetch content to the IoV edge. To enable content management based on mobility, we propose a novel Normalized Hidden Markov Model (NM-HMM) that anticipates a vehicle's future position. The framework also utilizes a mobility-aware collaborative filtering-based federated learning (FL) technique to enhance cache hit, reduce latency, and protect user privacy. To ensure secure cross-domain data sharing and mitigate the risk of data breaches, we also propose an extended ciphertext policy attribute-based encryption 

This article proposes a new framework called SpTFrame to achieve fast message dissemination using a software-defined vehicular networks (SDVNs) architecture along with a deep reinforcement learning (DRL) model. SpTFrame employs a convolutional neural network (CNN) and a gated recurrent unit (GRU) to detect spatio-temporal correlation under vehicle distribution on urban road networks. The novelty of the work is that it tackles short-term spatio-temporal volatility in SDVNs’ inherent characteristics and offers a way to handle short-term network topology changes. 

This paper investigates the feasibility of cache content prediction and coherence in the context of secure communication and search. We introduce a distributed multi-tier mobility-assisted edge intelligence based caching framework for the Internet of Vehicles (IoVs), called CacheIn. The proposed framework leverages user preferences, data correlations, and mobility information to prefetch content to the IoV edge. To enable content management based on mobility, we propose a novel Normalized Hidden Markov Model (NM-HMM) that anticipates a vehicle's future position. The framework also utilizes a mobility-aware collaborative filtering-based federated learning (FL) technique to enhance cache hit, reduce latency, and protect user privacy. To ensure secure cross-domain data sharing and mitigate the risk of data breaches, we also propose an extended ciphertext policy attribute-based encryption 

This paper presents an adaptive self-learning classifier-based clustering algorithm called AlcFier, to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate mobility and channel characteristics (i.e., orientation, adjacency, link availability, queue occupancy, and signal-to-noise ratio) into the clustering approach as a channel-aware metric to provide a new direction to the taxonomy of the approaches employed to handle cluster head election, cluster affiliation, and cluster administration challenges

This paper presents a hybrid machine learning (ML) and meta-heuristics (MH) based routing scheme called MetoidS to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate vehicle orientation-based unsupervised clustering and population based MH to provide a new direction to the taxonomy of the approaches to handling efficient route discovery and cluster maintenance challenges.

In this paper, we propose a lightweight anonymous authentication scheme called LAAS for UIo V using a bilinear map and lightweight cryptographic operation (i.e., one-way hash function, XOR, concatenation) to achieve a high level of security and privacy. Additionally, we propose an idea of batch message verification. 

In this research, we introduce a lightweight blockchain-based security protocol for secure communication and storage in SDN-enabled IoV, known as LBSV. The LBSV is a permissioned blockchain network that uses the proposed modified practical byzantine fault tolerance (mPBFT) consensus algorithm. Additionally, the SDN-enabled network exploits the blockchain framework and schedules different procedures. 

This paper proposes a spectral clustering technique along with the deep deterministic policy gradient (DDPG) algorithm using hybrid SDN architecture, called SeScR to enhance cluster stability and route selection method. 

This paper proposes a vehicle orientation based QoS routing in vehicular ad-hoc networks (VANETs), called OBQR that exploits vehicle orientational information instead of magnitude information. The cosine similarity concept with a preliminary scalarization model converts the multi-constraint objectives into a single constraint objective to find a set of possible paths to the destination.

In this paper, we proposed a cosine similarity based selective broadcast routing protocol, also known as CSBR, which leverages non-linear cluster formation ability using cosine similarity index. Distinct clusters and the coordinating vehicles assist each other in finding the most suitable path to reach the destination. Additionally, a probabilistic forwarding approach is used to disseminate routing messages further in the network.

In this paper, we propose adaptive self-learning clustering algorithm with reinforcement routing in SDVN known as RL-SDVN. An Expectation-Maximization model is used to predict a vehicle's movement and further Q-learning model is used to route data packets, so that vehicles in the same cluster coordinate with each other to find optimum routes. We evaluate our experimental results by comparing our approach with the clustering and self-learning based schemes proposed in the past.

This paper presents a lightweight authentication and batch verification scheme (LABVS) for UIoV using a bilinear map and cryptographic operations (i.e., one-way hash function, concatenation, XOR) to minimize the rate of message loss occurred due to delay in response time as in single message verification scheme. Subsequently, the scheme results in a high level of security and privacy.