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Vehicular ad hoc networks (VANETs) are crucial for infrastructure-less vehicular communication. The dynamic and complex nature of VANETs necessitates efficient routing strategies. This paper introduces an optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR) for VANETs. By utilizing the improved bald eagle optimization (IBEO) algorithm, we achieve optimal clustering that enhances energy efficiency. Cluster heads (CHs) are elected based on trust, calculated from metrics like energy consumption and signal strength. A Poplar optimization algorithm (POA) further refines these metrics. Subsequently, an optimal min–max recurrent neural network (OMM-RNN) determines the next forwarder nodes, facilitating data transfer in VANETs. Implemented using the NS-2 tool and SUMO traffic generator, OSC-GOR showcases optimized clustering and superior network efficiency. Comparative simulations reveal OSC-GOR’s advantages in energy use, latency, delivery ratio, goodput, and routing overhead, outperforming existing methods. Notably, OSC-GOR effectively counters VANET attacks such as GPS spoofing and Sybil threats.

Vehicular Ad Hoc Networks are mainly implemented to enable the interchange of huge amount of information among vehicles and between vehicles and control entities such as road side units or base stations, providing support for a comfortable and safe driving experience. However, due to the recent proliferation of cybersecurity threats, securing such a critical exchange of information becomes a fundamental prerequisite. In this paper, we propose a novel security scheme based on bilinear pairing-based cryptography to improve the security of the information exchanged in VANETs. Such scheme relies on the Elliptic Curve Discrete Logarithm Problem to provide anonymity and robust security features, and on Message Authentication Codes for verifying the vehicles’ identities. The proposed solution is able to achieve mutual authentication between involved entities and prevent impersonation, replay and insider attacks, at the expense of minimum overhead so that also big-data scale communications can be safely supported in the VANET environment.

Aim: The research aims at developing a traffic prediction and signal controlling model based on deep learning technique in order to provide congestion-free transportation in Intelligent Transport System (ITS).

Need for the Research: Recent technical advancements in the ITS, industrialization, and urbanization increase traffic congestion, which leads to high fuel consumption and health issues. This signifies the need for a dynamic traffic management system to handle the traffic congestion issues that negatively affect the transportation service.

Methods: For promoting congestion-free transportation in the ITS, this research aims to devise a traffic prediction and control system based on deep learning techniques that effectively controls the traffic during peak hours. The proposed mode-search optimization effectively clusters the vehicles based on the necessity. In addition, the mode-search optimization tunes the optimal hyperparameters of the deep Long Short Term Memory classifier, which minimizes the training loss. Further, the traffic signal control system is developed through the mode-search-based deep LSTM classifier for predicting the path of the vehicles by analyzing the attributes, such as velocity, acceleration, jitter, and priority of the vehicles.

Result: The experimental results evaluate the efficacy of the traffic prediction model in terms of quadratic mean of acceleration (QMA), jitter, standard deviation of travel time (SDTT), and throughput, for which the values are found to be 37.43, 0.23, 8.75, and 100 respectively.

Achievements: The proposed method attains the performance improvement of 5% to 42% when compared with the conventional methods.

In this work, spreading of information is investigated in a vehicular ad hoc network (VANET) by agent-based simulation. The proposed model is complex, containing two major states and some minor substates and additionally both reversible and irreversible state changes. According to our results, the spreading is really fast and widespread, the system can be in an up-to-date phase without external control. We show a very simple way to keep the system in this up-to-date status reducing dramatically the communication costs. Nevertheless, the topology of both the instantaneous and the aggregate communication network is also analyzed in this self-organized system and scale-free networks are found.

It is well known that Vehicular Adhoc Network (VANET) plays the major role in the field of Intelligent Transport System (ITS) to enhance road safety as well. The entire communication among vehicles is covered in VANETs. Since the number of researches is in progress for the enhancement of VANET model. Usually, the issues arising with this field is the achievement of multi-constrained Quality of Service (QoS) measures. To solve this issue, our paper intends to propose a cost model that considers certain network quality measures like travel, collision, congestion and the awareness about the QoS. Moreover, this paper adopts a renowned algorithm named Water Wave Optimization algorithm (WWO) to find the optimal route. The proposed method analyses its performance over other conventional methods like Lion Algorithm (LA), Genetic Algorithm (GA) and LA with Adaptive Mutation (LAAM) in terms of cost analysis, Convergence analysis and Computational time. Then the analytical results prove the superiority of proposed work in terms of decreased routing cost.

The growing number of automobiles on the road has now become a significant source of traffic, accidents, and pollution. Intelligent Transportation Systems (ITSs) could be the key to finding solutions that drastically reduce these issues. The linked vehicular networks channel is a fast-expanding topic for workflow management system and development. Traffic detection is a big issue on city streets. To make informed decisions in order to prevent traffic jams, one of the solutions is the Vehicular Ad-Hoc Network (VANET). We describe an approach for detecting traffic jams in both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, which provides vehicle drivers with multiple alternatives for determining the location of congestion, its size, and how to prevent becoming stuck in a traffic jam. The data are sent to the driver to assist him in making the appropriate selection. Delivering Quality of Service (QoS) for automotive networks is a difficult challenge due to the characteristics such as terms of transmission or high mobility, congested and fragmented channels, hardware defects, and a large number of vehicular devices. As a result, it is extremely desired to get and distribute resources efficiently. This research uses Reinforcement Learning with Decision-Making Model (RLDMM) method to enhance channel allocation. By using latency, Signal-to-Interference Ratio (SIR) and QoS, the available channel is initially determined. This method is used to determine suitable channel for platoon members. As a result, the proposed RLDMM achieves an SINR reduction of 29 Db, 69 kbps of throughput, 21.4% of collision probability with 4ms of latency.

Vehicular Ad-hoc Networks (VANETs) is a self-organized network formed through mutual communication between vehicles and other vehicles or roadside units. Broadcast is an important protocol in VANET, but it may cause some problems. This paper proposes an efficient broadcasting algorithm that is adaptive to both a straight road and road intersection scenarios, and also has a high accuracy rate, low delay and low consumption. It uses a number of the neighbor nodes' driving direction and the distance from the neighbor to itself as the metric to select a relay node. The simulation results show that the proposed algorithm has several advantages: less rebroadcast packets, broader transmission range, and higher channel utilization.

This chapter provides an overview of the Vehicular Ad-hoc Network (VANET) architecture and its Quality of Service (QoS). VANET has attracted a great deal of attention during the last decade because its communication protocols offer good potential for increasing safety and driver assistance. VANET as a foundation for Intelligent Transportation System (ITS) promises many improvements in terms of safety, traffic management, and infotainment. Among these three main categories, safety applications are the most important because they are designed to enhance safety and save lives. As such, safety applications are classified as real-time applications, which must react to events, e.g. accidents, within a certain period of time; otherwise, their implementation will be worthless. To achieve QoS, different layers of the VANET protocol stack have been targeted. This chapter focuses on two important groups of routing protocols, Ad-hoc On-demand Distance Vector (AODV) and Destination-Sequenced Distance Vector (DSDV) at the network layer. These routing protocols are investigated with respect to three QoS parameters: end-to-end delay, packet loss, and packet overhead.