Vehicle-to-vehicle (V2V) communication using Dedicated Short Range Communications (DSRC) technology promises to help drastically reduce vehicle collisions. DSRC allows vehicles in a highly mobile and complex network to send and receive safety messages with more reliability and lower latency compared with other wireless technologies used for automotive communications. However, there are many factors that could cause a safety-critical automotive application to become unreliable due to communication failures. While the reliability of V2V communication has been a subject of study by several researchers, there are still open questions regarding how the placement of the DSRC devices (inside or outside the host vehicle), the vehicle’s interior elements and the differences in altitude can affect the V2V communications. This article provides experimental testing data and analyses in order to quantify the impacts of relative vehicle speeds, altitude differences between vehicles, and interior obstacles on V2V communication range and reliability for opposite traffic, in both city and highway environments. We discuss how these results can adversely affect the design parameters of safety critical applications by considering the V2V application “Safe Pass Advisory” on two-lane rural highways.

Dedicated short-range communication (DSRC) systems, a form of vehicle-to-everything (V2X) systems, were placed on Utah Department of Transportation (UDOT) snowplows to request signal preemption. The study took place along five state routes in the Salt Lake City metropolitan area. Snowplows and intersections were equipped with the technology to communicate and process requests for signal preemption. Signal performance and vehicle performance analysis were performed to understand the impacts that snowplows requesting signal preemption had. Signal performance analysis was done to determine how snowplows with V2X systems using DSRC affected signals. Vehicle performance analysis was done to see if plowing and traffic efficiency and performance were improved, as well as evaluating safety implications of signal preemption. To perform the signal performance analysis, V2X data were collected to understand how often signal preemption was requested by snowplows, how often it was granted by signal controllers, and how long preemption requests affected signal controller timing. Snowplows requested preemption over 50 percent of the time they approached a signalized intersection. Of messages that requested signal preemption, over 80 percent were granted. On average, signal controllers are affected by preemption processing for less than 5 minutes. This shows that the system works as designed, is used often, and does not have adverse effects on signal controller. Data for vehicle performance analysis included analysis of snowplow speed data, general travel speed data, and crash data. These were collected to analyze the effects of snowplows requesting signal preemption on vehicle performance. The analysis showed that snowplow speeds are not changed due to the signal preemption system, but the number of times snowplows stopped was reduced. General travel speeds on equipped routes were more consistently closer to the speed limits than not equipped routes. Crash data showed a greater negative decrease on equipped routes than on not equipped routes. These findings showed minimal changes or impacts to vehicle performance, but anecdotal evidence from snowplow drivers indicates benefits from the system overall. There were various limitations in the analysis. Data granularity differed among datasets, making comparison between the different datasets difficult without reducing data integrity. Some datasets did not have much data, making statistical significance unclear. With these data limitations, conclusions were drawn, but do not fully describe all the potential benefits and impacts of snowplows with V2X systems that use DSRC to request signal preemption. Additional research is needed to better understand the impacts that snowplows requesting signal preemption has on different maintenance metrics, such as fuel usage and time spent plowing. It is also recommended that data used is explored for ways to improve the granularity.

Les activités de transport de passagers et de marchandises augmentent sans cesse dans le monde et en particulier dans l'Union Européenne, entre autres au bord des péages. Afin d'améliorer la fluidité et réduire les risques d'encombrements, une des solutions consiste à rendre les péages plus performants. L'objectif de cette thèse est d'améliorer la performance des systèmes d'identification de véhicules et de contribuer à la conception d'un système de classification des types de véhicules par ondes électromagnétiques pour application au télépéage. Ce système permet un paiement automatique sans arrêt des véhicules. La première partie de la thèse est consacrée à l'étude de deux systèmes d'identification de véhicules : RFID UHF et DSRC. Notre recherche s'est focalisée sur l'augmentation de la distance de communication ainsi que sur la réduction de la taille et du prix du système grâce à 5 nouvelles antennes à bas coûts, très directives et faciles à industrialiser. La deuxième partie est consacrée à l'étude d'un système de classification à distance des différents types de véhicules, basé sur les ondes diffusées par les véhicules. Il détecte la présence d'un véhicule et mesure la distance entre ce véhicule et le système avec une bonne précision. Ce système est basé sur la technique de radar Ultra-Large-Bande. Le signal émis est une impulsion monocyle de très courte durée. Dans cette partie, nous proposons et testons trois méthodes de classification de véhicules dans un environnement proche du milieu routier.

Security and privacy are among the growing concerns of a Vehicular Ad hoc Network (VANET) which requires a high degree of liability from its participants. In this dissertation, We address security, anonymity and privacy challenges of VANETs in the light of the IEEE standards for vehicular communications. VANET provides a variety of road-safety and other applications through wireless devices installed in vehicles and roadside infrastructure. A roadside infrastructure in VANET is generally public, and is prone to several different malicious attacks including node compromise, impersonation, and false message delivery attacks. Therefore, a user of a VANET must verify the integrity of a message that is delivered from a roadside infrastructure. On the other hand, a vehicle-originated message should be anonymous in order to ensure user-privacy in a VANET. However, a vehicle must not be able to take advantage of its anonymity for any misbehavior like sending false messages or malicious updates to other vehicles or a roadside infrastructure. We use proxy signature, identity-based signature, and elliptic curve cryptosystems to provide authentication for infrastructure generated messages, and anonymous authentication for vehicle originated messages. Authentication in a dense traffic condition is a challenge for a receiving entity as it incurs a processing delay at the receiving end. We address this issue with a dynamic approach that selectively verifies received messages based on a message's MAC-layer priority and a sender's information relevance. This approach makes a trade-off between priority and fairness in vehicular message authentication. We develop a network simulator to measure the impact of our authentication schemes over a WAVE protocol stack. Also, we investigate how some of the MAC-layer weaknesses may impair the security of a VANET. Our solutions are lightweight, bandwidth friendly and compatible to the current standards of vehicular communications.

Security and privacy are among the growing concerns of a Vehicular Ad hoc Network (VANET) which requires a high degree of liability from its participants. In this dissertation, We address security, anonymity and privacy challenges of VANETs in the light of the IEEE standards for vehicular communications. VANET provides a variety of road-safety and other applications through wireless devices installed in vehicles and roadside infrastructure. A roadside infrastructure in VANET is generally public, and is prone to several different malicious attacks including node compromise, impersonation, and false message delivery attacks. Therefore, a user of a VANET must verify the integrity of a message that is delivered from a roadside infrastructure. On the other hand, a vehicle-originated message should be anonymous in order to ensure user-privacy in a VANET. However, a vehicle must not be able to take advantage of its anonymity for any misbehavior like sending false messages or malicious updates to other vehicles or a roadside infrastructure. We use proxy signature, identity-based signature, and elliptic curve cryptosystems to provide authentication for infrastructure generated messages, and anonymous authentication for vehicle originated messages. Authentication in a dense traffic condition is a challenge for a receiving entity as it incurs a processing delay at the receiving end. We address this issue with a dynamic approach that selectively verifies received messages based on a message's MAC-layer priority and a sender's information relevance. This approach makes a trade-off between priority and fairness in vehicular message authentication. We develop a network simulator to measure the impact of our authentication schemes over a WAVE protocol stack. Also, we investigate how some of the MAC-layer weaknesses may impair the security of a VANET. Our solutions are lightweight, bandwidth friendly and compatible to the current standards of vehicular communications.

The primary goal of this thesis is to implement peer to peer technology in vehicular communication. The emerging concept of Vehicular Communication including road side infrastructure is a promising solution to avoid accidents and providing live traffic data. There is a high demand for the technologies which ensure low latency communication. Modern vehicles equipped with computing, communication and storage and sensing capabilities eased the transmission of data. To achieve deterministic bounds on data delivery, ability to be established anywhere quickly, and efficiency of data query we have chosen to implement a structured peer to peer overlay model in a cluster of vehicles. The vehicles in the cluster exchange information with the cluster head. The cluster head acts as a leader of the cluster, it fetches the data from the Road-side unit and other cluster heads. We have implemented Pyramid Tree Model in structured peer to peer models. A pyramid tree is group of clusters organized in a structured format with the data links between the clusters. The core concepts behind the pyramid tree model is clustering the nodes based on interest.

A sensitivity analysis was performed on the transit signal priority (TSP) requesting threshold to evaluate its impact on bus performance and general traffic. Two distinct bus routes were evaluated to determine the optimal requesting threshold that would balance the positive impacts on bus performance with the negative impacts on general traffic. Route 217, a conventional bus route, and the Utah Valley Express (UVX), a bus rapid transit line, utilize a dedicated short-range communication (DSRC)-based TSP system as part of their normal, day-to-day operations. Using field-generated data exclusively, bus performance and general traffic were evaluated over a 7-month period from February through August 2019. Bus performance was evaluated through on-time performance (OTP), schedule deviation, travel time, and dwell time, while the traffic analysis was performed by evaluating split failure, change in green time, and the frequency at which TSP was served. The requesting thresholds evaluated for Route 217 were 5-, 3-, 2-, and 0-minutes, which stipulate how far behind schedule the bus must be in order to request TSP. For UVX, 5-minutes and 2-minutes, as well as ON and OFF scenarios were evaluated; ON meant the buses were always requesting regardless of how late they were, while OFF meant that no requests were made and operations would be as if there were no TSP at all. A combination of observational and statistical analyses concluded with convincing evidence that OTP, schedule deviation, and travel time improve as the requesting threshold approaches zero with negligible impacts to general traffic. For Route 217, as the requesting threshold changed from 3, to 2, to 0 minutes, OTP increased 2.0 and 2.5 percent, respectively, mean schedule deviation improved 15.9 and 20.9 seconds, respectively, and travel time decreased at 72 percent of timepoints. Meanwhile, negative impacts to traffic occurred if an increase in split failure was measured after TSP was served, a phenomenon observed a maximum of once every 43 minutes. For UVX, as the requesting threshold changed from 5, to 2 minutes, to ON, OTP increased 7.6 and 4.7 percent, respectively, mean schedule deviation improved 24.3 and 15.0 seconds, respectively, and travel time decreased between 72 percent of timepoints. Thus, it is concluded that under the TSP system as implemented, bus performance improves as the requesting threshold approaches zero with inconsequential impacts to general traffic.

With recent advances in technology, Vehicular Ad-hoc Networks (VANETs) have grown in application. One of these areas of application is Vehicle Safety Communication (VSC) technology. VSC technology allows for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that enhance vehicle safety and driving experience. However, these newly developing technologies bring with them a concern for the vehicular privacy of drivers. Vehicles already employ the use of pseudonyms, unique identifiers used with signal messages for a limited period of time, to prevent long term tracking. But can attackers still attack vehicular privacy even when vehicles employ a pseudonym change strategy? The major contribution of this paper is a new attack model that uses long-distance pseudonym changing and short-distance non-changing protocols to associate vehicles with their respective pseudonyms.

Sharing a band of frequencies in the radio spectrum among multiple wireless systems has emerged as a viable solution for alleviating the severe capacity crunch in next-generation wireless mobile networks such as 5th generation mobile networks (5G). Spectrum sharing can be achieved by enabling multiple wireless systems to coexist in a single spectrum band. In this dissertation, we discuss the following coexistence problems in spectrum bands that have recently been raising notable research interest: 5G and Fixed Satellite Service (FSS) at 27.5-28.35 GHz (28 GHz); 5G and Fixed Service (FS) at 71-76 GHz (70 GHz); vehicular communications and Wi-Fi at 5.85-5.925 GHz (5.9 GHz); and mobile broadband communications and radar at 3.55-3.7 GHz (3.5 GHz). The results presented in each of the aforementioned parts show comprehensively that the coexistence methods help achieve spectrum sharing in each of the bands, and therefore contribute to achieve appreciable increase of bandwidth efficiency. The proposed techniques can contribute to making spectrum sharing a viable solution for the ever evolving capacity demands in the wireless communications landscape. / Ph. D.

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle's onboard unit (OBU). The main research question asks: 'What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?' The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network's probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models. / Ph. D. / The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle’s onboard unit (OBU). The main research question asks: “What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?” The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network’s probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models.