A Smartphone VANET Based Forward Collision Detection System

Along with the various technological advancements, the next generation vehicular networks such as the Internet of Vehicles (IoV) also bring in various cybersecurity challenges. To effectively address these challenges, in addition to the existing authentication techniques, there is also a need for identification of the misbehaving entities in the network. This letter proposes a deep learning-based classification framework to identify potential misbehaving vehicles before the communication requests from the On Board Units (OBUs) of the vehicles can be entertained by the network infrastructure such as the Road Side Units (RSUs). The evaluated metrics demonstrate the performance of the proposed classification approaches.

Finding a vacant place to park cars in the rush hour is time-consuming and even may be frustrating for the drivers. In some studies, vehicles are equipped with communicative tools called On Board Units (OBUs), which come along with roadside devices known as Roadside Units (RSUs), which allow the drivers to communicate each other and trace a vacant parking place easily. Previous systems work with connection of sensors all over the road and parking space and may result in spending much time to find a parking place, occupancy of the empty parking place until the car gets to the desired location, requirement for an additional hardware, wired network communication, and security issues. In this paper, we propose an exponential smoothing and multi-objective decision-making by using cloud-based methods to find the best parking place, taking into consideration of the park-cost for the driver. The proposed system uses the cellular base stations to eliminate the cost of the RSUs and the sensors. The results of our simulations via NS-2 network simulator confirm the efficiency of the proposed model.

In urban vehicular network, existing routing protocols using beacon message scheme and P2P (peer to peer) density discovery scheme can hardly make a good performance as well as keep a high feasibility. This paper proposes an IGOR (improved geography and overlay routing) in urban vehicular environment. In IGOR, RSUs (road side units) are introduced to support the road density discovery. A passive neighbor discovery scheme is used by OBUs (on board units) instead of the beacon message scheme. The performance of IGOR is evaluated against the benchmark routing protocol of LOUVRE (landmark overlays for urban vehicular routing environment). Results show that IGOR has a higher accuracy in density discovery as well as a lower cost of the routing establishment and maintenance.

Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

Vehicular ad hoc networks (VANETs) have recently received considerable attentions for both safety and commercial applications. In this paper, we address popular content distribution (PCD) in VANETs, in which a popular file is downloaded by a group of on-board units (OBUs) passing by a roadside unit (RSU). Due to high speeds and channel fadings, the OBUs may not finish downloading the entire file from the RSU, but only possess several content pieces. In this paper, we propose a cooperative approach using coalitional graph game to establish a peer-to-peer (P2P) network among the OBUs, in which content pieces are exchanged to complete the file downloading process. Further, we adopt cognitive radio (CR) technique for implementing vehicle-to-vehicle (V2V) transmissions. Simulation results show that the proposed algorithm enjoys a better performance in various conditions, compared to the non-cooperative approach, where OBUs randomly response to content requests from other OBUs.

As urbanization grows and the cost of vehicle production decreases, urban traffic has become a major problem of modern life. Developing intelligent vehicles alongside standardizing inter-vehicle communications promises that this technology will be a part of future life. In this research, we propose a method in which clustering is used to gather vehicles' movements information in a vehicle ad-hoc network. This method is based on extending green wave using road-side units as a fixed agent and on board units (OBU) in vehicles as a mobile agent. This information is then transmitted to traffic lights for decision making. This algorithm evaluated using Monte Carlo simulation. The simulation results show this method has a positive effect on reducing the average waiting time and overall stop of the vehicles behind the traffic lights.

With the existing Long Term Evolution(LTE) based Cellular Vehicle to Anything(C-V2X) infrastructure and the recent advancements in 5G implemented Ultra Reliable Low Latency Communication(URLLC), comes the need of fast and secure communications in Connected Autonomous Vehicles(CAVs). Physical layer can always be a fast and reliable medium for exploitation while having legit communication as a source for performance and latency optimization besides the enhanced spectrum efficiency depicted by multicasting and its adequate worth. Utilization of CAVs as connected IoT resources for load mitigation is also eminent from research. In this article, we benefit the 5G V2X environment of CAVs for road safety use case where a secure geo-specific locally containerized co-operative aware autonomous driving system would be the prime concern. In the localized V2X server, Roadside units(RSUs) get benefit from physical layer parameters to generate a key for end to end(E2E) communication while the On Board Unit(OBU) containers provide the cryptographic shreds as microservices in real-time as they do while monitoring or location updating. Mininet and RYU as network emulator and controller are used for the scenario implementation and analysis. Beyond RSU, no need of secure communication exists any longer due to the broadcast nature of messages for all involved vehicles in the marked premises so instead of security, we put prime focus on least latency due to highly dynamic environment and we did zero forced sparse channel estimation for evolved multimedia broadcast multicast service(eMBMS) in C-V2X.

Global misbehavior detection in Cooperative Intelligent Transport Systems (C–ITS) is carried out by a central entity named Misbehavior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle’s On–Board Units (OBUs) and by Road–Side Units (RSUs) called Misbehavior Reports (MBRs) to the MA. By analyzing these Misbehavior Reports (MBRs), the MA is able to compute various misbehavior detection information. In this work, we propose and evaluate different Machine Learning (ML)-based solutions for the internal detection process of the MA. We show through extensive simulation and several detection metrics the ability of solutions to precisely identify different misbehavior types.

This paper proposes a roadside unit (RSU)-coordinated synchronous multi-channel medium access control (MAC) scheme for vehicular ad hoc networks (VANETs). The proposed scheme allows the on board unit (OBU) to reserve service channels (SCHs) on the control channel (CCH) during almost the whole synchronous interval and supports simultaneous transmissions on different SCHs. It enhances the performance of VANETs and decreases the CCH congestion. Moreover, we use RSU to record the rendezvous information and broadcast it to the OBUs. This method avoids the multi-channel hidden terminal problem. An analytical model is developed to evaluate the aggregate throughput on SCHs. This model considers the following factors: non-safety message transmission probability in each synchronous interval, average size of the non-safety message, and the number of OBUs. Furthermore, the requirement of obtaining the maximum throughput is computed. Simulation results are provided to validate the analytical model and to demonstrate the improvement in throughput. The results indicate that the proposed scheme can provide higher aggregate throughput than that of the vehicular enhanced multi-channel MAC and IEEE 1609.4, and better performance in CCH congestion control especially under high network load conditions.

One of the most important components of intelligent transportation systems (ITS) is the automotive self-organizing VANET network (vehicular ad hoc network). Its nodes are vehicles with specialized onboard units (OBU) installed on them. Such a network can be subject to various attacks. To reduce the effectiveness of a number of attacks on the VANET, it is advisable to use authentication protocols. Well-known authentication protocols support a security policy with full trust in roadside unit (RSU) base stations. The disadvantage of these authentication protocols is the ability of the RSU to track the route of the vehicle. This leads to a violation of the privacy and anonymity of the vehicle’s owner. To eliminate this drawback, the article proposes an adaptive authentication protocol. An advantage of this protocol is the provision of high imitation resistance without using symmetric and asymmetric ciphers. This result has been achieved by using a zero-knowledge authentication protocol. A scheme for adapting the protocol parameters depending on the intensity of the user’s traffic has been developed for the proposed protocol. The scientific novelty of this solution is to reduce time spent on authentication without changing the protocol execution algorithm by reducing the number of modular exponentiation operations when calculating true and “distorted” digests of the prover and verifying the correctness of responses, as well as by reducing the number of responses. Authentication, as before, takes place in one round without changing the bit depth of the modulus used in the protocol. To evaluate the effectiveness of the adaptive authentication protocol, the VANET model was implemented using NS-2. The obtained research results have shown that the adaptation of the authentication protocol in conditions of increased density of vehicles on the road makes it possible to increase the volume of data exchange between OBU and RSU by reducing the level of confidentiality. In addition, a mechanism for verifying the authority of the vehicle’s owner for provided services has been developed. As a result of the implementation of this mechanism, vehicle registration sites (VRS) calculate the public key of the vehicle without using encryption and provide necessary services to the owner.

ALICIA: Applied Intelligence in blockchain based VANET: Accident Validation as a Case Study

This paper proposes and assesses a Big Data Platform for effective storage and analysis of On Board Unit (OBU) data related to the mobility of trucks in Belgium. The large volume and the streaming nature of the OBU data requires the setup of a big data platform for an efficient collection, storage and analysis. The solution relies on i) the Hadoop Distributed File System (HDFS) to store data, ii) the Apache Parquet format for data compression and columnar storage, and iii) Spark for parallel and streaming processing of data. Data replication, compression and columnar storage ensure robustness to node failure, data distribution, and faster access to data.

The vehicle industry manufacturer is showing an increased interest in remote diagnostics by improving customer relationship management and commercial interest. The special nature of construction machines fact that architectures and applications proposed for vehicular communication are not optimized for these types of vehicles. This paper proposes a new remote diagnostic architecture based on IEEE 802.11n for construction machines communication to the infrastructure on site. The mobile Internet is used to transmit collected data to the central monitoring application. We discuss the possible network selection algorithms, mobility and security protocols that can be implemented on the On Board Units (OBU). The proposed architecture is based on Host Identity Protocol (HIP). This protocol introduces a Host Identifier (HI) for naming the endpoints and use intensive cryptographic operation for security and mobility management. Due to their minimal computing capabilities, many single board computers used as OBU themselves suffer from applications which need intensive CPU usage. Thus, a performance analysis of HIP, implemented on single board computers, is proposed by the end of this paper.

Validation and Verification of WLAN Medical Devices

Two hundred college students participated in an experiment investigating how individuals use television and real world experiences to construct alcohol use schema. Students were asked to write a story about a student who had a difficult day and, on arriving home, fixed himself a drink. In randomly assigned conditions, directions led the student to believe this was to be either a real world scenario or the setting for a television episode. Stories were coded for setting and social nature of use, quantity of use, consequences (both severity and valence), and overall tone. In general, stories characterized alcohol use as heavy, social, relaxing, and resulting in mostly minor consequences. Story characteristics did not differ based on direction type (real world vs. TV scenario). Students also completed a series of measures regarding television viewing habits, attitudes about alcohol use, and past experiences with use. Regarding television viewing patterns, those who watched more television overall created stories with more alcohol use. Viewing of dramas in particular was associated with less severe consequences as a result of use. In terms of personal experience with use, individuals who had driven under the influence of alcohol were more likely to construct a story with a positive tone and those who had passed out from drinking were less likely to conclude their stories with a moral tale discouraging drinking. The conclusion of this research is that both personal and observed experiences are important influencing factors in individuals' schema regarding alcohol use.