Vehicle self-positioning via Kalman filter using multi-station non-circular signals

Road safety applications envisaged for vehicular ad hoc networks (VANETs) depend largely on the exchange of messages to deliver information to concerned vehicles. Safety applications as well as inherent VANET characteristics make data dissemination an essential service and a challenging task. We are developing a decentralized efficient solution for broadcast data dissemination through two game-theoretical mechanisms. Besides, VANETs can also include autonomous vehicles (AVs). AVs might represent a revolutionary new paradigm that can be a reality in our cities in the next few years. AVs do not need a driver to work; instead, they should copy a proper human behavior to adapt the driving according to the current circumstances, such as speed limit, pedestrian crossing street or wheather conditions. We will develop an AV software module including artificial intelligence (AI) techniques so that AVs can interact with the dynamic scenario throughout time. Finally, we also will include electrical vehicles (EV) in the VANET, so that special services such as finding and reserving an EV charging station place will be welcome. In addition, we are developing a multimetric geographic routing protocol for VANETs to transmit H.265 video (traffic accident, traffic state, commercial….) over VANETs.

A vehicular ad hoc network (VANET) is a special kind of mobile ad hoc networks (MANET) that supports cooperative driving among communicating cars on the roads. There are several technologies involved in implementation of wireless communication in VANETs. These technologies include 3G cellular systems, long-term evolution (LTE), LTE Advanced, IEEE 802.16, and IEEE 802.11. This chapter focuses on 802.11-based wireless communication in VANETs. It reviews some presented methods for vehicle movement path prediction, and explains some algorithms are explained for performance improvement in opportunistic networks based on the predicted vehicle movements. The chapter considers the fact that communication in VANETs can be infrastructure-based or infrastructureless. It also reviews some methods to apply the predicted vehicles' position for improving communication in infrastructure-based and infrastructureless VANETs, respectively. Opportunistic routing algorithms can be classified according to their dependency on roadside infrastructure. The chapter presents the taxonomy of opportunistic routing in VANETs.

Vehicular Ad-hoc Network (VANET) communications are mobile communication alternatives allowing vehicles to exchange information among them with a suitable forwarding scheme. The forwarding scheme is a mechanism to select proper candidate nodes as the intermediate node. Current angle-based forwarding schemes take into account a Horizontal Relative Angle (HRA) and propose Vertical Relative Angle (VRA) as an alternative. The introduction of a filtering concept, implementing both HRA and VRA, restricts the number of insignificant candidates. Under various VANET communication factors (e.g., network density, mobility, and road level topology) the work presented here investigates the VRA scheme to improve the forwarding decision especially in the three-dimensional case within a large city environment.

Vehicular Ad hoc Network (VANET) is a component of the Intelligent Transportation System (ITS) which furnishes communication among vehicles. It delivers comfort and safety information to passengers and vehicle drivers. Security plays a vital role during the transmission of data as the various distinct security attacks directly influence the safety of the passengers on the road. Several security attacks will disrupt normal functions like the transmission of data. Some security breaches inject false information which affects the safety of drivers. However, there are several demurrers in detecting abnormal activities efficiently as the traditional system faces imbalanced data problems. This paper presents an Intrusion Detection System (IDS) for detecting anomalies and protecting communication systems from several distinct attacks. The proposed IDS utilizes two deep learning (DL) mechanisms such as Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) for detecting anomalies in the vehicular network. The hybrid CNN-GRU mechanism helps in solving the overfitting and low-velocity problems. It also detects several attacks and protects the ad hoc network from those attacks to safeguard the vehicle users. The results show that the propounded model outperforms the other traditional detection mechanisms by 10.79% in terms of performance compared to other IDS.

Vehicular ad hoc network (VANET) plays a vital role in the intelligent transportation system (ITS). When a vehicle receives a message through network, the CRL (certificate revocation list) checking process will operate before certificate and signature verification. After successful authentication, a CRL list is created based on authentication. This CRL is used to verify whether a vehicle node can be permitted for communication in the VANET network. But when using CRL, a huge amount of storage space and checking time is needed. So we proposed a method without the CRL list. It mentions a key management list to overcome large storage space and checking time. Even it reduced the access delay. For access permission we can do an authentication system based digital novel signature authentication (DNSA) for each vehicle in the VANET with the RSU unit or with other participant node vehicles in the communication as per the topology. We can perform an efficient and secured communication in VANET.

Intelligent transportation system (ITS) applications may rely on the reliable infrastructure that vehicular ad hoc networks (VANETs) offer. Roadside units (RSUs) are the main component of vehicle-to-vehicle and vehicle-to-infrastructure links in VANET communication. When cognitive radio (CR) VANET investigations were analysed, two major performance problems were found: low connection stability because of the high vehicle movement and excessive energy consumption. We suggest a fresh strategy to deal with these issues: Mobility and Link Reliability Estimation Using CSS AND ML CR-VANETs, known as MLRECSS CR-VANET. MLRECSS CR-VANET consists of four main components: CR-VANET construction, Cooperative spectrum Sensing model, Speed-based mobility prediction, link-based multipath routing. First, we create CSS-based CR-VANETs to examine and address issues with power consumption and spectrum scarcity in VANETs. Vehicle speed forecasts and fluctuations are assessed via mobility prediction. Lastly, routing is made reliable and effective by utilizing the machine learning and ad hoc on-demand multipath distance vector (AOMDV) routing protocol in combination with link stability based multipath routing (LSMR). MLRECSSCR-VANET technique outperforms the previous methods by 3.69% in terms of packet delivery ratio,7.21% in terms of residual energy, 6.09% in terms of throughput, 8.33% in terms of residual node speed and 13.97% in terms of energy efficiency. Comparing it to more contemporary efforts like LMCCR-VANET, SCCR-VANET, CFCR-VANET, and MMCR-VANET, it shows improved energy efficiency, delivery rates, decreased energy consumption, end-to-end latency, and routing overhead.

Vehicular ad-hoc network (VANET) technology enables communication between vehicles, or vehicles and road-side units (RSUs) through wireless communication devices installed on the vehicles. One of the most important goals of VANETs is providing safety applications for passengers. In addition, VANETs provide comfort applications to users. Guaranteeing a reliable and stable routing protocol over VANETs is a very important step. The proposed research attempts to improve routing protocols that decrease the end-to-end delay to suit VANET communication characteristics. In addition, it proposes analysis of the end-to-end delay probability distribution. More specifically, we derive a closed-form expression for the probability distribution of the re-healing delay in a VANET conditioned on the distance between two VANET clusters. Furthermore, we propose a closed-form expression for the probability distribution of the unconditional re-healing delay. Moreover, we develop a mathematical model to calculate the probability distribution of the end-to-end delay. On the other hand, using Unmanned Aerial Vehicles (UAVs) or drones in wireless communications and Vehicular Ad-hoc Networks (VANETs) has started to attract attention. We propose a routing protocol that uses infrastructure drones for boosting VANET communications to achieve a minimum vehicle-to-drone packet delivery delay. In addition, we propose a closed-form expression for the probability distribution of the vehicle-to-drone packet delivery delay on a two-way highway. Moreover, based on that closed-form expression, we can calculate the minimum drone density (maximum separation distance between two adjacent drones) that stochastically limits the worst case of the vehicle-to-drone packet delivery delay. Furthermore, we propose a drones-active service (DAS) that is added to the location service in a VANET. This service dynamically and periodically obtains the required number of active drones based on the current highway connectivity state by obtaining the maximum distance between each two adjacent drones while satisfying a probabilistic constraint for vehicle-todrone packet delivery delay. Our analysis focuses on two-way highway VANET networks with low vehicular density. The simulation results show the accuracy of our analysis and reflect the relation between the drone density, vehicular density and speed, other VANET parameters, and the vehicle-to-drone packet delivery delay. In addition, we propose a new routing protocol called multi-copy intersection-based routing (MCIR) for vehicular ad-hoc networks (VANETs) in urban areas. MCIR is an intersectionbased routing protocol that forwards multiple copies of the packets in different road segments. Moreover, it is a beacon-less routing protocol with a carry-and-forward strategy. We show via simulation that the MCIR protocol is superior to other existing routing protocols, especially in low vehicular density scenarios. The results show that MCIR achieves a shorter end-to-end delay and a higher packet delivery ratio in urban VANET communications.

Vehicular ad hoc networks (VANETs), an application of the Internet of Things (IoT) providing a better intelligent transportation system (ITS), has received substantial attention from both industry and academia. Heterogeneous vehicular communications in VANETs occur when both vehicles and the infrastructure use different cryptographic technologies to exchange safety messages. However, the safety messages between vehicles and the infrastructure are communicated wirelessly; therefore, security issues are a serious concern in this domain. The existing schemes secure the transmission of safety messages with respect to confidentiality, authentication, and nonrepudiation. However, they are inappropriate with respect to efficiency. They cause computational overhead on the receiver and bandwidth overhead in the communications. To cope with this, we propose an elliptic curve cryptosystem-based hybrid signcryption (ECCHSC) protocol that satisfies the security requirements (i.e., message confidentiality, message’s source authentication, message integrity, nonrepudiation, and identity–anonymity) for heterogeneous vehicle-to-infrastructure (V2I) communications in a single logical step. This protocol allows secure transmission of a safety message from a vehicle using identity-based cryptography (IDC) to a roadside unit (RSU) using public-key infrastructure (PKI). In addition, the ECCHSC protocol enables the RSU to receive multiple ciphertexts, aggregate them, and de-signcrypt them simultaneously through the batch de-signcryption method, which further improves the performance. The ECCHSC protocol has indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) and existential unforgeability against adaptive chosen message attacks (EUF-CMAs) in the random oracle model (ROM). The performance analysis of our protocol demonstrates a significant reduction in computational overhead and in communication/storage overhead as compared to the state-of-the-art schemes.

Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey

Intelligent Transportation Systems (ITS) are nowadays considered very important applications of smart cities. One of the most important technologies that are utilized to support ITS is Vehicular Ad-hoc Networks (VANETs). In VANETs, vehicles communicate with each other (V2V) or with the infrastructure (Roadside Units) (V2I). Roadside Units (RSUs) collect data from vehicles in the coverage area and send it to cloud servers through the Internet. Cloud servers have high performance computational and storage capabilities that ITS applications require for data processing. However, due to the real-time requirements of the ITS applications, cloud approach alone cannot be guaranteed to satisfy the strict time constraints due to long latency access of the centralized cloud server. Fog Computing is an emerging approach that extends the services of cloud computing to the edge of the network. Fog Computing can be utilized in VANETs through deployment of fog nodes into RSUs. One of the major challenges is identifying the optimum number, locations and computational capabilities of the RSUs particularly in urban regions where obstacles exist heavily inside the coverage area of the RSUs. In this paper, we consider the optimization problem of fog-based RSU placement where the objective is to maximize the achieved level of service quality in a cost-effective way. The problem is formulated as a Satisfiability Modulo Theories (SMT) problem and solved using Microsoft Z3. The proposed approach is able to generate a set of solutions as Pareto front. We obtained data from OpenStreetMap for Cairo city. Our approach outperforms other solutions in the literature in terms of cost.

Emerging as the effective strategy of intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) have the capacity of drastically improving the driving experience and road safety. In typical VANET scenarios, high mobility and volatility of vehicles result in dynamic topology of vehicular networks. That is, individual vehicle may pass through the effective domain of multiple neighboring road-side-units (RSUs) during a comparatively short time interval. Hence, efficient and low-latency cross-domain verification with all the successive RSUs is of significance. Recently, a lot of research on VANET authentication and key distribution was presented, while the critical cross-domain authentication (CDA) issue has not been properly addressed. Particularly, the existing CDA solutions mainly reply on the acquired confidential keying information from the neighboring entities (RSUs and vehicles), while too much trustworthiness is granted to the involved RSUs. Please note that the RSUs are distributively located and may be compromised or disabled by adversary, thus vital vehicle information may be revealed. Furthermore, frequent data interactions between RSUs and cloud server are always the major requisite so as to achieve mutual authentication with cross-domain vehicles, which leads to heavy bandwidth consumption and high latency. In this paper, we address the above VANET cross-domain authentication issue under the novel RSU edge networks assumption. Please note that RSUs are assumed to be semi-trustworthy entity in our design, where critical vehicular keying messages remain secrecy. Homomorphic encryption design is applied for all involved RSUs and vehicles. In this way, successive RSUs could efficiently verify the cross-domain vehicle with the transited certificate from the neighbor RSUs and vehicle itself, while the identity and secrets of each vehicle is hidden all the time. Afterwards, dynamic updating towards the anonymous vehicle identity is conducted upon validation, where conditional privacy preserving is available. Moreover, pairing-free mutual authentication method is used for efficiency consideration. Formal security analysis is given, proving that the HCDA mechanism yields desirable security properties on VANET cross domain authentication issue. Performance discussions demonstrate efficiency of the proposed HCDA scheme compared with the state-of-the-art.

Vehicular ad hoc networks (VANETs) are an increasing important paradigm for greatly enhancing roadway system efficiency and traffic safety. To widely deploy VANETs in real life, it is critical to deal with the security and privacy issues in VANETs. In this paper, we propose a certificateless conditional privacy preserving authentication (CCPPA) scheme based on certificateless cryptography and elliptic curve cryptography for secure vehicle-to-infrastructure communication in VANETs. In the proposed scheme, a roadside unit (RSU) can simultaneously verify plenty of received messages such that the total verification time may be sharply decreased. Furthermore, the security analysis indicates that the proposed scheme is provably secure in the random oracle model and fulfills all the requirements on security and privacy. To further improve efficiency, both map-to-point hash operation and bilinear pairing operation are not employed. Compared with previous CCPPA schemes, the proposed scheme prominently cuts down computation delay of message signing and verification by 66.9%–85.5% and 91.8%–93.4%, respectively, and reduces communication cost by 44.4%. Extensive simulations show that the proposed scheme is practicable and achieves prominent performances of very little average message delay and average message loss ratio and thus is appropriate for realistic applications.

The technology is growing towards smart communication through smart devices. This smart communication leads to the development of Vehicular Ad Hoc Network (VANET). These days each vehicle is acting as a smart device which could establish a smart communication among the vehicles. The growth of VANET communication passes through various stages such as RoadSide Unit (RSU), Vehicle to Vehicle (V2V), Cluster based, Internet of Vehicles (IoV), and Web VANET (WVANET) Communication models. All these advancements in VANET architecture provide a smart communication among the vehicles. The vital aim of VANET architecture is to provide an efficient and effective emergency and warning alerts to the vehicles so that the vehicle can take appropriate decisions without any delay to safeguard the safety of the passengers. However, it will be more reliable to the vehicles if the VANET architecture could provide a live emergency and warning alerts to the vehicles in well ahead of time. In order to provide live emergency and warning alerts the communication as well as the device should be smart. In this paper, we have developed a live emergency and warning alerts to the vehicles though android application. Each vehicle will have the android application installed on it, in which the entire live driving scenario is provided. Once, the live driving scenario is provided, the live emergency and warning alerts can be shown to the vehicles in well ahead of time. As live emergency alerts are shown to the vehicles, it will help the vehicles to take the right decision more effectively.

Localization in Vehicular Adhoc Networks (VANETs) has witnessed significant attention due the instantaneous failure of Global Positioning System (GPS). Performance of Intelligent Transport Systems (ITS) applications is based on efficient and reliable information dissemination techniques. Efficiency and reliability of information dissemination is dependent on the availability of accurate location information about vehicles. Most of the localization techniques suggested in the past are based on either real time radio signal measurement or Road Side Unit (RSU). Signal measurement is performed in terms of Time of Arrival (ToA) or Received Signal Strength Indicator (RSSI) which is susceptible to measurement error due to the high mobility of vehicles in vehicular traffic environment. Availability of RSU can not be guaranteed. This paper proposes a Geometry based Inter Vehicle Distance (Geo-IVD) calculation techniques which can be utilized for information dissemination in VANETs. Geo-IVD is a cooperative technique which utilizes past and present knowledge about the vehicle. It utilizes mathematical geometry to calculate present unknown location of a vehicle. More specifically, Geo-IVD find the current unknown location of a vehicle considering two cases. In the first case, vehicle's current direction is considered to be the same or opposite from its past direction. In the second case, vehicle's current direction is considered different (other than opposite) from its past direction. Mathematical geometry is utilized to solve the problem of finding current location in both these cases. Simulations are carried out in ns2 and results are comparatively analyzed with that of the state-of-the-art technique: ToA-IVD. Analysis of results attests the superiority of Geo-IVD to the state-of-the-art technique.

Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc networks, preventing nodes from accessing accurate road information. In order to assure the reliability and trustworthiness of information through the networks, assessing the credibility of nodes has become a critical task in vehicular ad hoc networks. A new scheme for malicious node detection is proposed in this work. Multiple factors are fed into a fuzzy logic model for evaluating the trust for each node. Vehicles are divided into clusters in our approach, and a road side unit manages each cluster. The road side unit assesses the credibility of nodes before accessing vehicular ad hoc networks. The road side unit evicts a malicious node based on trust value. Simulations are used to validate our technique. We demonstrate that our scheme can detect and evict all malicious nodes in the vehicular ad hoc network over time, lowering the ratio of malicious nodes. Furthermore, it has a positive impact on selfish node participation. The scheme increases the success rate of delivered data to the same level as the ideal cases when no selfish node is present.