The total lifecycle of a vehicle contains many phases, from production to sales to first customer to second customer and so on until the end of life. Each one of these phases includes different activities in different business areas and under different conditions. This means that the customers´ needs will vary depending on which lifecycle phase the vehicle is in and the offered services have to be adapted to this. Therefore it is important for truck developing companies to know when a transition, from one lifecycle phase to another has occurred. This study is based on a case study provided by Scania, a company that develops trucks and busses. Delimitations were that the study would focus on connected long-haulage trucks that are in Europe under their first life cycle phase, that the developed services would be described on a conceptual level and not cover any economic aspects. With this in mind, the following research questions were created: RQ1) What defines a transition phase? RQ2) How can the long-haulage trucks’ usage pattern be used to identify a transition phase? RQ3) Which data is needed to identify a transition phase? RQ4) Based on the results of RQ2 and RQ3, how could the transition alert service be designed? RQ5) Which applications could the transitions alert service be used for? The study included a literature study covering product lifecycle theory, servicification, second-hand market, big data, telematics, intelligent vehicles and statistic hypothesis testing. Further, two truck drivers were observed in order to get better understanding of the transportation business and the truck driving activities. Two qualitative interview studies were made with hauliers, service salesmen, truck salesmen and distributors from Czech Republic, Denmark, Italy, Poland, Spain and Sweden. The results of the empirical studies were analysed and RQ1 could be answered. Transition phase is the period between two different vehicle owners and/or two different ways of utilizing the truck. The analysis also gave a good picture of how the trucks are used during their life and in the transition phases, which gave an idea about usage patterns that could answer RQ2. The answer was formulated as something named phase-DNA, composed by six parameters that should change during a transition phase: Geography, Route, Driver, Traffic Condition, Assignments and Services. Through a group brainstorming with experts in connected services, ideas of which data that could be used to describe each one of the parameters in the phase-DNA were found. These were sorted and evaluated until at least one data type for each parameter was set. The specific data types were chosen because they reflected their parameter well and because they were data that were accessible in order to conduct tests and validations. The final set of data types consisted of: Route Shape, Amount of Stops, Run Time, Idle Time, Distance Driven, Coasting, Driver ID, Average Speed, Fuel Consumption and Workshop History Data. This set of data types was used for the formulation of a hypothesis, that said that after a transition phase at least some of these data types should change. This was also the point where RQ3 was answered. II The hypothesis was analysed using an exploratory analysis by plotting all the data types over time and observing if a change could be seen close to the change of ownership. The result showed that Amount of Stops and Driver ID were the most indicative data types, these two were further analysed with a statistical hypothesis test and a visualisation method. The results were used to develop an algorithm that is able to give an indication if a transition phase has occurred. The algorithm searches for changes in the six data types: Driver ID, Amount of Stops, Run Time, Distance Driven, Idle Time and Route Shape. The results from the empirical studies were used to define requirements for the development of a service based on the information of phase transition called transition alert service (TAS), which is the answer to RQ4. Furthermore possible stakeholders that could be interested in the transition phase information were investigated together with an examination of their needs. TAS fulfils the five main needs identified from the stakeholders: ease start and cancellation of services, avoid unnecessary telecom expenses, avoid that information goes to the wrong customer, find new customers and customize services. In order to solve this, an algorithm detecting a transition phase was developed; it was done by searching for changes in the six data types: Driver ID, Amount of Stops, Run Time, Distance Driven, Idle Time and Route Shape. Moreover if the TAS information is combined with other information it could be used for creating new services. Through different idea generation workshops a large number of new ideas and concepts were generated, which became the answer to RQ5. In total eleven applications for the transition alert service were developed: nine connected to change in ownership and two connected to change in utilization. Additionally, one support service named "Vehicle History" that is based on collected historical TAS was created. Further, one total solution named "No Worries Second-Hand" was created that includes five of the developed services. This total solution offers the customer the perfectly suitable second-hand truck without having to spend time searching for it. It also consists of a contract saying that if the customer signs a R&M contract, the dealer will buy back the vehicle and offer a new used vehicle when the old one gets too old or used. TAS makes this total solution possible by giving the dealer access to information about the truck and through this predict phase transitions. In conclusion, the developed services and especially the combination of them into a total solution would, according to the authors, favour the transition from a product focused company to a total solution provider, and extend the knowledge about the second-hand market.

"In this PhD dissertation, we propose distributed adaptation mechanisms for connected vehicles to deal with the connectivity challenges. To understand the system behavior of the solutions for connected vehicles, we first need to characterize the operational environment. Therefore, we devised a large scale fading model for various link types, including point-to-point vehicular communications and multi-hop connected vehicles. We explored two small scale fading models to define the characteristics of multi-hop connected vehicles. Taking our research into multi-hop connected vehicles one step further, we propose selective information relaying to avoid message congestion due to redundant messages received by the relay vehicle. Results show that the proposed mechanism reduces messaging load by up to 75% without sacrificing environmental awareness. Once we define the channel characteristics, we propose a distributed congestion control algorithm to solve the messaging overhead on the channels as the next research interest of this dissertation. We propose a combined transmit power and message rate adaptation for connected vehicles. The proposed algorithm increases the environmental awareness and achieves the application requirements by considering highly dynamic network characteristics. Both power and rate adaptation mechanisms are performed jointly to avoid one result affecting the other negatively. Results prove that the proposed algorithm can increase awareness by 20% while keeping the channel load and interference at almost the same level as well as improve the average message rate by 18%. As the last step of this dissertation, distributed cooperative dynamic spectrum access technique is proposed to solve the channel overhead and the limited resources issues. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this work by using a computationally efficient numerical approach. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors. An interdisciplinary approach referred to as entropy-based weighting is used for defining the neighbor credibility. Once the vehicle accesses the channel, we propose a decision mechanism for channel switching that is inspired by the optimal flower selection process employed by bumblebees foraging. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero."

No-notice evacuations of metropolitan areas can place significant demands on transportation infrastructure. In preparation, emergency managers and transportation engineers study potential demands and many create evacuation traffic management plans. The findings from a St. Louis Metro East evacuation study revealed some problematic areas of the transportation network. At these locations the traffic backed up during a simulated evacuation, caused a significant amount of delay, and increased the evacuation clearance time. An emerging paradigm called Connected Vehicle (CV) technology can provide real-time communication between vehicles in a traffic stream. The objectives of this research were to evaluate the impacts of CVs on evacuation from a downtown metropolitan area. The microsimulation software VISSIM was used to model the roadway network and the evacuation traffic. The model was built, calibrated and validated for studying the performance of traffic during the evacuation. This model helped researchers to find the time required to evacuate people in this area for different disaster scenarios. Because it is unlikely that vehicles equipped with CV technologies will become commonplace soon, the researcher tested different levels of deployment, also known as penetration rate. This study included penetration rates from 0 to 30 percent CVs; evaluating the average speed, average and total delays. The findings suggest significant reductions in total delays when CVs reached a penetration rate of 30 percent or greater. Results showed that the presence of CVs at a penetration rate of 30 percent could reduce the overall traffic delay by 60 percent over the evacuation period. A sensitivity analysis was conducted and the finding showed that a 10 percent increase in the penetration rate will significantly improve traffic flow. The findings of this study suggest that the communication capabilities of CVs can reduce delays and improve the traffic flow rate during a no-notice evacuation. Additionally, the benefits could be greater for evacuations with higher volumes, evacuations that last longer, and evacuations with higher proportions of CVs in the vehicle stream.

Internet of Things (IoT) has great potential in enabling many beneficial applications (i.e., connected vehicle applications). Named Data Networking (NDN) recently emerges as a promising networking paradigm in supporting IoT due to its data-centric architecture. In this dissertation, we present our research on design a scalable, efficient and secure ndn-based data retrieval framework for Internet of Things. Our work includes three parts:First, we envision an NDN-based Connected Vehicles (CV) application framework with a distributed data service model, as CV is a typical scenario of IoT. The scalability of the framework is greatly challenged by the fast mobility and vast moving area of vehicles. To handle such an issue, we develop a novel hyperbolic hierarchical NDN backbone architecture (H2NDN) by exploiting the location dependency of CV applications. H2NDN designs the backbone routers topology and the data/interest namespace by following the hierarchical architecture of geographic locations. The efficient data searching only requires static forwarding information base (FIB) configuration over NDN routers. To avoid overloading high-level routers, H2NDN integrates hyperbolic routing through carefully designed hyperbolic planes.Second, a distributed adaptive caching strategy is proposed to improve the efficiency of data caches on NDN routers. NDN provides native support to cache data at routers for future Interest packets. As we model the caching problem, the goal of cache allocation is to maximize the savings of Interest/Data forwarding hops under the limited cache space on each router. We discuss the impracticality of global optimization and provide the local caching method. Extensive ndnSIM based simulation with real traffic data proves the efficiency and scalability of the proposed H2NDN architecture.Finally, although NDN provides some security advantages such as secures data directly and uses name semantics to enable applications to reason about security, employing NDN to support IoT applications nevertheless presents some new challenges about security. In this dissertation, we focus on two resultant attacks that are not effectively handled in current studies, namely the targeted blackhole attack and the targeted content poisoning attack. We propose a lightweight and efficient approach named SmartDetour to tackle the two attacks. To ensure high scalability and collusion-resilience, SmartDetour lets each router respond to attacks (i.e., packet drops or corrupted data) independently in order to isolate attackers. The core solution contains a reputation-based probabilistic forwarding strategy and a proactive attacker detection algorithm. Extensive ndnSIM based simulation demonstrates the efficiency and accuracy of the proposed SmartDetour.

It is widely believed that the advances of Vehicle-to-Vehicle (V2V) communications will help to remodel the prospect of road transportation systems. By virtue of V2V communications, information generated by the vehicle control system, on-board sensors or passengers can be effectively disseminated among vehicles in proximity, or to vehicles in multiple hops away in a vehicular ad-hoc network (VANET). Without assistance from any built infrastructure, a variety of active road safety applications (e.g., Vehicle-Based Road Condition Warning, Cooperative Collision Warning, Approaching Emergency Vehicle Warning) and traffic efficiency management applications (e.g., Wrong Way Driver Warning) are enabled by inter-vehicle wireless links. The purpose of connecting vehicle technologies is to improve road safety, awareness, and transportation systems efficiency. The Wireless Access for Vehicular Environments (WAVE) technology/Dedicated Short-Range Communications (DSRC) is the main enabling wireless technology for both V2V and vehicle-to-Infrastructure (V2I) communications. From USDOT and stakeholders detailed analysis, it is resolved that WAVE is the only viable option for critical safety and other low latency mobility and environmental applications. WAVE technology has reached to a mature stage and a basic V2V system is expected to be deployed in the next few years. In the late part of 2015, USDOT announce that WAVE is sufficiently robust to proceed with the preparation for deployment of connected vehicle environments. The USDOT has created a roadmap with preliminary plans to guide industries and public agencies implementation efforts. However, there are persisting major concerns regarding the V2V initiative needing more analysis and testing. One of the concerns is the channel congestion. Channel congestion may impact WAVE effectiveness, which may in turn impact the effectiveness of supported safety applications. Suggested solutions to mitigate congestion are focused on supporting adaptive control of the message transmission power. The Institute of Electrical and Electronics Engineers (IEEE), and European Telecommunications Standards Institute (ETSI) have included transmit power component per packet to be used for channel congestion control mechanism. The adjustment of transmission powers has created an environment of vehicles with different transmission powers. Such environment will affect the performance of the proposed protocols to disseminate warning messages. It may also affect the performance of periodic beaconing that is required by most of the safety applications. Thus far, several protocols have been proposed to help identify appropriate relay vehicles. However, such approaches neglect the fact that vehicle transmission ranges are typically heterogeneous due to different transmission power values or dynamic adjustment of power to alleviate congestion. The proper selection of relay nodes governs high delivery ratio, acceptable overall end-to-end delay and efficient bandwidth usage. In this work, area-based relay selection protocols that work in heterogeneous transmission powers are introduced. Mathematical functions are developed for a timer and decision probability to be used by each vehicle receiving the message. The values of the two functions allow the vehicle to determine if it is the next to act as relay node or not. Geometric taxonomy for all possible overlap patterns in wireless environment is constructed with the related math calculations. Moreover, an adaptive expiry time for neighbours-table entries that harmonizes with dynamic beacon scheduling is proposed.

Connected vehicle networks and future autonomous driving systems call for characterization of risky driving events to improve safety applications and autonomous driving features. Precision of driving event characterization (\gls{dec}) systems in connected vehicles has become increasingly important with the responsive connectivity that is available to the modern vehicles. While risky behavior patterns entail potential safety issues on road networks, the advent of vehicular sensing and vehicular networks cannot guarantee accurate characterization of driving/movement behavior of vehicles and the precision of such systems still remains an open issue. Additionally, artificial intelligence-backed solutions are vital components towards highly accurate characterization systems in the modern transportation. However, such solutions require significant volume of driving event data for an acceptable level of performance. With this in mind, the proposal of this thesis is three-fold: 1) a reliable methodology to generate representative data under the scarcity of diverse anomalous sensory data, 2) classification of mobility/driving events of vehicles with attention-based deep learning methods, and 3) a modular prior-knowledge input method to the characterization methodologies in order to further improve the trustworthiness of the systems. Implementing the proposed steps, we are able to not only increase the consistency in the training process but also reduce the false positive detection instances compared to the previous models. One of the roadblocks against robust event characterization systems in connected vehicles that is tackled in this thesis is the scarcity of anomalous driving data to make the training of event classification models robust. To mitigate this issue an optimized deep recurrent neural network-based encoding model is introduced to extract the precise feature representation of the anomalous data. The utilization of the encoded input to the previous network allowed for a 12\% accuracy improvement. Furthermore, we introduced a framework for precise risky driving behavior detection that takes advantage of an attention-based neural networks model. Ultimately, the combination of prior knowledge modelling with our network and some optimizations to the network structure, the model outperforms the state-of-the-art solutions by reaching an average accuracy of 0.96 and F1-score of 0.92.

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities.

The safety and efficiency of vehicular communications rely on the correctness of the data exchanged between vehicles. Location spoofing is a proven and powerful attack against Vehicle-to-everything (V2X) communication systems that can cause traffic congestion and other safety hazards. Recent work also demonstrates practical spoofing attacks that can confuse intelligent transportation systems at road intersections. In this work, we propose two self-reliant schemes at the application layer and the physical layer to detect such misbehaviors. These schemes can be run independently by each vehicle and do not rely on the assumption that the majority of vehicles is honest. We first propose a scheme that uses application-layer plausibility checks as a feature vector for machine learning models. Our results show that this scheme improves the precision of the plausibility checks by over 20% by using them as feature vectors in KNN and SVM classifiers. We also show how to classify different types of known misbehaviors, once they are detected. We then propose three novel physical layer plausibility checks that leverage the received signal strength indicator (RSSI) of basic safety messages (BSMs). These plausibility checks have multi-step mechanisms to improve not only the detection rate, but also to decrease false positives. We comprehensively evaluate the performance of these plausibility checks using the VeReMi dataset (which we enhance along the way) for several types of attacks. We show that the best performing physical layer plausibility check among the three considered achieves an overall detection rate of 83.73% and a precision of 95.91%. The proposed application-layer and physical-layer plausibility checks provide a promising framework toward the deployment of on self-reliant misbehavior detection systems.

The dynamic nature of vehicular ad hoc network (VANET) induced by frequent topology changes and node mobility, imposes critical challenges for vehicular communications. Aggravated by the high volume of information dissemination among vehicles over limited bandwidth, the topological dynamics of VANET causes congestion in the communication channel, which is the primary cause of problems such as message drop, delay, and degraded quality of service. To mitigate these problems, congestion detection, and control techniques are needed to be incorporated in a vehicular network. Congestion control approaches can be either open-loop or closed loop based on pre-congestion or post congestion strategies. We present a general architecture of vehicular communication in urban and highway environment as well as a state-of-the-art survey of recent congestion detection and control techniques. We also identify the drawbacks of existing approaches and classify them according to different hierarchical schemes. Through an extensive literature review, we recommend solution approaches and future directions for handling congestion in vehicular communications.

The Internet of Things (IoT) is a vision for an internetwork of intelligent, communicating objects, which is on the cusp of transforming human lives. Smart transportation is one of the critical application domains of IoT and has benefitted from using state-of-the-art technology to combat urban issues such as traffic congestion while promoting communication between the vehicles, increasing driver safety, traffic efficiency and ultimately paving the way for autonomous vehicles. Connected Vehicle (CV) technology, enabled by Dedicated Short Range Communication (DSRC), has attracted significant attention from industry, academia, and government, due to its potential for improving driver comfort and safety. These vehicular communications have stringent transmission requirements. To assure the effectiveness and reliability of DRSC, efficient algorithms are needed to ensure adequate quality of service in the event of network congestion. Previously proposed congestion control methods that require high levels of cooperation among Vehicular Ad-Hoc Network (VANET) nodes. This paper proposes a new approach, VANETomo, which uses statistical Network Tomography (NT) to infer transmission delays on links between vehicles with no cooperation from connected nodes. Our proposed method combines open and closed loops congestion control in a VANET environment. Simulation results show VANETomo outperforming other congestion control strategies.