Recently, vehicular ad hoc networks (VANETs) embark a great deal of attention in the area of wireless and communication technology and are becoming one of the prominent research areas in the intelligent transportation system (ITS) because they provide safety and precautionary measures to the drivers and passengers, respectively. VANETs are quite different from the mobile ad hoc networks (MANETs) in terms of characteristics, challenges, system architecture, and their application. In this paper, we summarize the recent state-of-the-art methods of VANETs by discussing their architecture, security, and challenges. Secondly, we discuss the detailed analysis of security schemes and the possible measures to provide secure communication in VANETs. Then, we comprehensively cover the authentication schemes, which is able to protect the vehicular network from malicious nodes and fake messages. Thus, it provides security in VANETs. Thirdly, we cover the mobility and network simulators, as well as other simulation tools, followed by the performance of authentication schemes. Finally, we discuss the comfort and safety applications of VANETs. In sum, this paper comprehensively covers the entire VANET system and its applications by filling the gaps of existing surveys and incorporating the latest trends in VANETs.

Vehicular networks are becoming a prominent research field in the intelligent transportation system (ITS) due to the nature and characteristics of providing high-level road safety and optimized traffic management. Vehicles are equipped with the heavy communication equipment which requires a high power supply, on-board computing device, and data storage devices. Many wireless communication technologies are deployed to maintain and enhance the traffic management system. The ITS is capable of providing services to the traffic authorities and precautionary measures to the drivers and passengers. Several methods have been proposed for discussing the security and privacy issues for the vehicular ad hoc networks (VANETs) and vehicular cloud computing (VCC). They receive a great deal of attention from researchers around the world since they are new technologies, and they can improve road safety and enhance traffic flow by utilizing the vehicles resources and communication system. Firstly, the VANETs are presented, including the basic overview, characteristics, threats, and attacks. The location privacy methodologies are elaborated, which can protect the confidential information of the vehicle, such as the location detail and driver information. Secondly, the trust management models in the VANETs are comprehensively discussed, followed by the comparison of the cryptography and trust models in terms of different kinds of attacks. Then, the simulation tools and applications of the VANETs are discussed, and the evolution is presented from the VANETs to VCC in the vehicular network. Thirdly, the VCC is discussed from its architecture and the security and privacy issues. Finally, several research challenges on the VANETs and VCC are presented. In sum, this survey comprehensively covers the location privacy and trust management models of the VANETs and discusses the security and privacy issues in the VCC, which fills the gap of existing surveys. Also, it indicates the research challenges in the VANETs and VCC.

Traffic classification is considered an important research area due to the increasing demand in network users. It not only effectively improve the network service identifications and security issues of the traffic network, but also provide robust accuracy and efficiency in different Internet application behaviors and patterns. Several traffic classification techniques have been proposed and applied successfully in recent years. However, the existing literature lack of comprehensive survey which could provide an overview and analysis towards the recent developments in network traffic classification. To this end, this survey presents a comprehensive investigation on traffic classification techniques by carefully reviewing existing methods from a new perspective. We comprehensively discuss the procedures and datasets for traffic classification. Additionally, traffic criteria are proposed, which could be beneficial to assess the effectiveness of the developed classification algorithm. Then, the traffic classification techniques are discussed in detail. Then, we thoroughly discussed the machine learning (ML) methods for traffic classification. For researcher’s convenience, we present the traffic obfuscation techniques, which could be helpful for designing a better classifier. Finally, key findings and open research challenges for network traffic classification are identified along with recommendations for future research directions. In sum, this survey fills the gap of existing surveys and summarizes the latest research developments in traffic classification.

Traffic control strategy plays a significant role in obtaining sustainable objectives because it not only improves traffic mobility but also enhances traffic management systems. It has been developed and applied by the research community in recent years and still offers various challenges and issues that may require the attention of researchers and engineers. Recent technological developments toward connected and automated vehicles are beneficial for improving traffic safety and achieving sustainable goals. There is a need to develop a survey on traffic control techniques, which could provide the recent developments in the traffic control strategy and could be useful in obtaining sustainable goals. This survey presents a comprehensive investigation of traffic control techniques by carefully reviewing existing methods from a new perspective and reviews various traffic control strategies that play an important role in achieving sustainable objectives. First, we present traffic control modeling techniques that provide a robust solution to obtain reasonable traffic and sustainable mobilities. These techniques could be helpful for enhancing the traffic flow in a freeway traffic environment. Then, we discuss traffic control strategies that could be helpful for researchers and practitioners to design a robust freeway traffic controller. Second, we present a comprehensive review of recent state-of-the-art methods on the vehicle design control strategy, which is followed by the traffic control design strategy. They aim to reduce traffic emissions and energy consumption by a vehicle. Finally, we present the open research challenges and outline some recommendations which could be beneficial for obtaining sustainable goals in traffic systems and help researchers understand various technical aspects in the deployment of traffic control systems.

Traffic incident detection is one of the major research areas of intelligent transportation systems (ITSs). In recent years, many mega-cities suffer from heavy traffic flow and congestion. Therefore, monitoring traffic scenarios is a challenging issue due to the nature and the characteristics of a traffic incident. Reliable detection of traffic incidents and congestions provide useful information for enhancing traffic safety and indicate the characteristics of traffic incidents, traffic violation, driving pattern, etc. This paper investigates the estimation of traffic incident from a hybrid observer (HO) method, and detects a traffic incident by using an improved automatic incident detection (AID) technique based on the lane-changing speed mechanism in the highway traffic environment. First, we developed the connection between vehicles and roadside units (RSUs) by using a beacon mechanism. Then, they will exchange information once the vehicles get access to a wireless medium. Second, we utilized the probabilistic approach to collect the traffic information data, by using a vehicle to infrastructure (V2I) communication. Third, we estimated the traffic incident by using an HO method which can provide an accurate estimation of an event occurring. Finally, in order to detect traffic incident accurately, we applied the probabilistic data collected through V2I communication based on lane-changing speed mechanism. The experimental results and analysis obtained from simulations show that the proposed method outperforms other methods in terms of obtaining a better estimation of traffic incident which agrees well with the theoretical incident, around 30% faster detection of traffic incidents and 25% faster dissipation of traffic congestion. With regard to duration of an incident, the proposed system obtained a better Kaplan–Meier (KM) curve, influenced by the shortest duration of time to clear the traffic incident, in comparison with the other methods.