Biblio

Vehicle Ad-hoc Networks (VANETs) have recently become an active research area. This is because of its important applications in the transportation field in which vehicles have severe position during activities of daily living in persons. In this paper, the basic background of the VANET from the Intelligent Transportation System (ITS), Mobile Ad-hoc Networks (MANETs), VANET standard and VANET characteristics are discussed. Second, the architecture from components and communications of the system are presented. Then, the critical challenges and future perspectives in this field are comprehensively reviewed. This paper could serve as a guide and reference in the design and development of any new techniques for VANETs. Moreover, this paper may help researchers and developers in the selection of the main features of VANET for their goals in one single document.

Wireless communication in the field of radio frequency (RF) have modernized our society. People experience persistent connection and high-speed data through wireless technologies like Wi-Fi and LTE while browsing the internet. This causes congestion to network; users make it difficult for everyone to access the internet or to communicate reliably on time. The major issues of RF spectrum are intrusion, high latency and it requires an individual transmitter receiver setup in order to function. Dr. Herald Hass came up with an idea of `data through illumination'. Surmounting the drawbacks of RF spectrum, visible light communication (VLC) is more favored technique. In intelligent transportation system (ITS), this evolving technology of VLC has a strong hold in order to connect vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links wirelessly. Indoor VLC applications have been studied deeply while the field of vehicular VLC (V-VLC) networking is relatively a less researched domain because it has greater level of intrusion and additive ambient light noise is higher in outdoor VLC. Other factors due to which the implementation of VLC faces a lot of hurdles are mostly related to environment such as dust, haze, snow, sunlight, rain, fog, smog and atmospheric disturbances. In this paper, we executed a thorough channel modelling in order to study the effects of clear weather, fog, snow and rain quantitatively with respect to different wavelengths in consideration for an ITS. This makes ITS more robust in nature. The parameters under consideration will be signal-to-noise ratio (SNR), bit error rate (BER) and optical power received (OPR) for different LED wavelengths.

The paper considers data security issues in vehicle-to-infrastructure communications, where vehicles stream data to a road side unit. We assume aggregated data in road side units can be stored or used for data analytics. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicle layer, where a group leader is assigned to communicate with group devices and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality of sensory data.

Many technological cases exploiting data science have been realized in recent years; machine learning, Internet of Things, and stream data processing are examples of this trend. Other advanced applications have focused on capturing the value from streaming data of different objects of transport and traffic management in an Intelligent Transportation System (ITS). In this context, security control and trust level play a decisive role in the sustainable adoption of this trend. However, conceptual work integrating the security approaches of different disciplines into one coherent reference architecture is limited. The contribution of this paper is a reference architecture for ITS security (called SITS). In addition, a classification of Big Data technologies, products, and services to address the ITS trust challenges is presented. We also proposed a novel multi-tier ITS security framework for validating the usability of SITS with business intelligence development in the enterprise domain.

Internet of Vehicle (IoV) is an essential part of the Intelligent Transportation system (ITS) which is growing exponentially in the automotive industry domain. The term IoV is used in this paper for Internet of Vehicles. IoV is conceptualized for sharing traffic, safety and several other vehicle-related information between vehicles and end user. In recent years, the number of connected vehicles has increased allover the world. Having information sharing and connectivity as its advantage, IoV also faces the challenging task in the cybersecurity-related matters. The future consists of crowded places in an interconnected world through wearable's, sensors, smart phones etc. We are converging towards IoV technology and interactions with crowded space of connected peoples. However, this convergence demands high-security mechanism from the connected crowd as-well-as other connected vehicles to safeguard of proposed IoV system. In this paper, we coin the term of smart people crowd (SPC) and the smart vehicular crowd (SVC) for the Internet of Vehicles (IoV). These specific crowds of SPC and SVC are the potential cyber attackers of the smart IoV. People connected to the internet in the crowded place are known as a smart crowd. They have interfacing devices with sensors and the environment. A smart crowd would also consist of the random number of smart vehicles. With the future converging in to the smart connected framework for crowds, vehicles and connected vehicles, we present a novel cyber-physical surveillance system (CPSS) framework to tackle the security threats in the crowded environment for the smart automotive industry and provide the cyber security mechanism in the crowded places. We also describe an overview of use cases and their security challenges on the Internet of Vehicles.

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture's future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.

Vehicular Ad-hoc Networks (VANETs) play an essential role in ensuring safe, reliable and faster transportation with the help of an Intelligent Transportation system. The trustworthiness of vehicles in VANETs is extremely important to ensure the authenticity of messages and traffic information transmitted in extremely dynamic topographical conditions where vehicles move at high speed. False or misleading information may cause substantial traffic congestions, road accidents and may even cost lives. Many approaches exist in literature to measure the trustworthiness of GPS data and messages of an Autonomous Vehicle (AV). To the best of our knowledge, they have not considered the trustworthiness of other On-Board Unit (OBU) components of an AV, along with GPS data and transmitted messages, though they have a substantial relevance in overall vehicle trust measurement. In this paper, we introduce a novel model to measure the overall trustworthiness of an AV considering four different OBU components additionally. The performance of the proposed method is evaluated with a traffic simulation model developed by Simulation of Urban Mobility (SUMO) using realistic traffic data and considering different levels of uncertainty.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.

The paper considers data security and privacy issues in intelligent transportation systems which involve data streams coming out from individual vehicles to road side units. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicular layer, where a group leader is assigned to communicate with group members and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality and privacy of sensory data.

While the general idea of Intelligent Transportation System (ITS) is to employ suitable, sophisticated information and communications technologies, however, such tool also encompass many system complexities. Fittingly, this paper aims to highlight the most contemporary system complications of ITS and in doing so, will also underline the safety, security and risk management concerns. More importantly, effectively treating such issues will ultimately improve the reliability and efficiency of transportation systems. Whereas such issues are among the most significant subjects for any intelligent system, for ITS in particular they the most dominant. For such intelligent systems, the safety, security and risk management issues must not only be decidedly prioritized, but also methodically integrated. As a part of such ITS integration, this paper will delicately examine the Emergency Management System (EMS) development and application. Accurate EMS is not only a mandatory feature of intelligent systems, but it is a fundamental component of ITS which will vigilantly respond to safety, security and risk management apprehensions. To further substantiate such scheme, the Sydney Metro's EMS will be also conferred. It was determined that, the Sydney Metro's EMS although highly advanced, it was also vigilantly aligned with specific designated safety, security and risk management strategies.

In Vehicle-to-Vehicle (V2V) communications, malicious actors may spread false information to undermine the safety and efficiency of the vehicular traffic stream. Thus, vehicles must determine how to respond to the contents of messages which maybe false even though they are authenticated in the sense that receivers can verify contents were not tampered with and originated from a verifiable transmitter. Existing solutions to find appropriate actions are inadequate since they separately address trust and decision, require the honest majority (more honest ones than malicious), and do not incorporate driver preferences in the decision-making process. In this work, we propose a novel trust-aware decision-making framework without requiring an honest majority. It securely determines the likelihood of reported road events despite the presence of false data, and consequently provides the optimal decision for the vehicles. The basic idea of our framework is to leverage the implied effect of the road event to verify the consistency between each vehicle's reported data and actual behavior, and determine the data trustworthiness and event belief by integrating the Bayes' rule and Dempster Shafer Theory. The resulting belief serves as inputs to a utility maximization framework focusing on both safety and efficiency. This framework considers the two basic necessities of the Intelligent Transportation System and also incorporates drivers' preferences to decide the optimal action. Simulation results show the robustness of our framework under the multiple-vehicle attack, and different balances between safety and efficiency can be achieved via selecting appropriate human preference factors based on the driver's risk-taking willingness.

Connected cars have received massive attention in Intelligent Transportation System. Many potential services, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by cars. Without a secure authentication algorithm, malicious cars may send out invalid spatial-temporal messages and then deny creating them. Meanwhile, a lot of private information may be disclosed from these spatial-temporal messages. Since cars move on expressways at high speed, any authentication must be performed in real-time to prevent crashes. In this paper, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) mechanism to ensure these properties. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is distributed and mostly designed on symmetric-key cryptography and an entropy-based commitment, and is able to fast authenticate spatial-temporal messages. FastTrust also ensures the anonymity and unlinkability of spatial-temporal messages by developing a pseudonym-varying scheduling scheme on cars. We provide both analytical and simulation evaluations to show that FastTrust achieves the security and privacy properties. FastTrust is low-cost in terms of communication and computational resources, authenticating 20 times faster than existing Elliptic Curve Digital Signature Algorithm.

Vehicular Ad-Hoc Network (VANET) is a form of Peer-to-Peer (P2P) wireless communication between vehicles, which is characterized by the high mobility. In practice, VANET can be utilized to cater connections via multi-hop communication between vehicles to provide traffic information seamlessly, such as traffic jam and traffic accident, without the need of dedicated centralized infrastructure. Although dedicated infrastructures may also be involved in VANET, such as Road Side Units (RSUs), most of the time VANET relies solely on Vehicle-to-Vehicle (V2V) communication, which makes it vulnerable to several potential attacks in P2P based communication, as there are no trusted authorities that provide authentication and security. One of the potential threats is a Sybil attack, wherein an adversary uses a considerable number of forged identities to illegitimately infuse false or biased information which may mislead a system into making decisions benefiting the adversary. Avoiding Sybil attacks in VANET is a difficult problem, as there are typically no trusted authorities that provide cryptographic assurance of Sybil resilience. This paper presents a technique to detect and mitigate Sybil attacks, which requires no dedicated infrastructure, by utilizing just V2V communication. The proposed method work based on underlying assumption that says the mobility of vehicles in high vehicle density and the limited transmission power of the adversary creates unique groups of vehicle neighbors at a certain time point, which can be calculated in a statistical fashion providing a temporal and spatial analysis to verify real and impersonated vehicle identities. The proposed method also covers the mitigation procedures to create a trust model and announce neighboring vehicles regarding the detected tempered identities in a secure way utilizing Diffie-Hellman key distribution. This paper also presents discussions concerning the proposed approach with regard to benefits and drawbacks of sparse road condition and other potential threats.

This paper address the problem of shadow detection and removal in traffic vision analysis. Basically, the presence of the shadow in the traffic sequences is imminent, and therefore leads to errors at segmentation stage and often misclassified as an object region or as a moving object. This paper presents a shadow removal method, based on both color and texture features, aiming to contribute to retrieve efficiently the moving objects whose detection are usually under the influence of cast-shadows. Additionally, in order to get a shadow-free foreground segmentation image, a morphology reconstruction algorithm is used to recover the foreground disturbed by shadow removal. Once shadows are detected, an automatic shadow removal model is proposed based on the information retrieved from the histogram shape. Experimental results on a real traffic sequence is presented to test the proposed approach and to validate the algorithm's performance.