Vehicles are more frequently being built with hardware that supports wireless communica- tion capabilities. Dedicated short-range communications (DSRC) is a standard that enables the hardware on vehicles to communicate with one another directly rather than through external infrastructure such as a cellular tower. With DSRC supporting small-range communications, multi-hop routing is utilized when a packet needs to reach a long-range destination. A vehicular ad-hoc network (VANET) broadcast protocol (VBP) was developed. This thesis introduces VBP, an open-source framework for simulating multi-hop routing on mobile and wireless vehicular networks. VBP is built for the routing layer of the network simulation tool called network simulator 3 (ns-3) and contains a custom protocol that adapts to various traffic conditions on a roadway. To test VBP we ran six simulations across three traffic levels. Results confirm that VBP successfully routes packets or queues packets when a first or next hop is not available. The development process of VBP is documented to help researchers who are trying to create a custom routing protocol for ns-3.

<p>This thesis proposes different scheduling algorithm to be implemented on the Roadside Units in ITS environment. Both variable and constant bit rate cases are considered.</p> / <p>Vehicular Ad-hoc Networks (VANETs) will be an integral part of future Intelligent Trans- portation Systems (ITS). In highway settings where electrical power connections may not be available, road-side infrastructure will often be powered by renewable energy sources, such as solar power. For this reason, energy efficient designs are desirable.</p> <p>This thesis considers the problem of energy efficient downlink scheduling for road- side infrastructure. In the first part of the thesis, the constant bit rate (CBR) air interface case is investigated. Packet-based and timeslot-based scheduling models for the theoretical minimum energy bound are considered. Timeslot-based scheduling is then formulated as a Mixed Integer Linear Program (MILP). Following this, three energy efficient online scheduling algorithms with varying complexity are introduced. Results from a variety of experiments show that the proposed scheduling algorithms perform well when compared to the energy lower bounds.</p> <p>In the second part of the thesis, the variable bit rate (VBR) air interface option is considered. Offline scheduling formulations are derived that provide lower bounds on the energy required to fufill vehicle requests. An integer linear program (ILP) is introduced which can be solved to find optimal offline VBR schedules. Two flow graph based models are then introduced. The first uses Generalized Flow (GF) graphs and the second uses time expanded graphs (TEGs) to model the scheduling problem. Four online scheduling algorithms with varying energy efficiency, fairness and computational complexities are developed. The proposed algorithms’ performance is examined under different traffic scenarios and they are found to perform well compared to the lower bound.</p> / Doctor of Philosophy (PhD)

The effective management of forces within heavy vehicles is essential for achieving desired performance outcomes. In this study, an auto-generated Model Predictive Control Allocation (MPCA) algorithm is presented. The controller is designed to distribute forces among individual actuators in a vehicle, focusing primarily on longitudinal forces while exploring lateral force dynamics. The approach integrates models of the actuators with vehicle dynamics, encompassing both point mass and dynamic vehicle models, within the controller framework. Through simulation, proof of the MPC's superior performance in reference tracking could be demonstrated, especially in comparison with baseline simulations employing force ratio split (FRS) and equal split (ES) distribution methods. Furthermore, findings show that it was possible to achieve a more energy efficient force distribution using the MPCs.

The development of autonomous ground and aerial vehicles has driven the requirement for radio access technologies (RATs) to support low latency applications. While onboard sensors such as Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), and cameras can sense and assess the immediate space around the vehicle, RATs are crucial for the exchange of information on critical events, such as accidents and changes in trajectory, with other vehicles and surrounding infrastructure in a timely manner. Simulations and analytical models are critical in modelling and designing efficient networks. In this dissertation, we focus on (a) proposing and developing algorithms to improve the performance of decentralized vehicular communications in safety critical situations and (b) supporting these proposals with simulation and analysis of the two most popular RAT standards, the Dedicated Short Range Communications (DSRC) standard, and the Cellular vehicle-to-everything (C-V2X) standard. In our first contribution, we propose a risk based protocol for vehicles using the DSRC standard. The protocol allows a higher beacon transmission rate for vehicles that are at a higher risk of collision. We verify the benefits of the risk based protocol over conventional DSRC using ns-3 simulations. Two risk based beacon rate protocols are evaluated in our ns-3 simulator, one that adapts the beacon rate between 1 and 10 Hz, and another between 1 and 20 Hz. Our results show that both protocols improve the packet delivery ratio (PDR) performance by up to 45% in congested environments using the 1-10 Hz adaptive beacon rate protocol and by 38% using the 1-20 Hz adaptive scheme. The two adaptive beacon rate protocol simulation results also show that the likelihood of a vehicle collision due to missed packets decreases by up to 41% and 77% respectively, in a three lane dense highway scenario with 160 vehicles operating at different speeds. In our second contribution, we study the performance of a distance based transmission protocol for vehicular ad hoc network (VANET) using tools from stochastic geometry. We consider a risk based transmission protocol where vehicles transmit more frequently depending on the distance to adjacent vehicles. We evaluate two transmission policies, a listen more policy, in which the transmission rate of vehicles decreases as the inter-vehicular distance decreases, and a talk more policy, in which the transmission rate of vehicles increases as the distance to the vehicle ahead of it decreases. We model the layout of a highway using a 1-D Poisson Point process (PPP) and analyze the performance of a typical receiver in this highway setting. We characterize the success probability of a typical link assuming slotted ALOHA as the channel access scheme. We study the trends in success probability as a function of system parameters. Our third contribution includes improvements to the 3rd Generation Partnership Project (3GPP) Release 14 C-V2X standard, evaluated using a modified collision framework. In C-V2X basic safety messages (BSMs) are transmitted through Mode-4 communications, introduced in Release 14. Mode-4 communications operate under the principle of sensing-based semi-persistent scheduling (SPS), where vehicles sense and schedule transmissions without a base station present. We propose an improved adaptive semi-persistent scheduling, termed Ch-RRI SPS, for Mode-4 C-V2X networks. Specifically, Ch-RRI SPS allows each vehicle to dynamically adjust in real-time the BSM rate, referred to in the LTE standard as the resource reservation interval (RRI). Our study based on system level simulations demonstrates that Ch-RRI SPS greatly outperforms SPS in terms of both on-road safety performance, measured as collision risk, and network performance, measured as packet delivery ratio, in all considered C-V2X scenarios. In high density scenarios, e.g., 80 vehicles/km, Ch-RRI SPS shows a collision risk reduction of 51.27%, 51.20% and 75.41% when compared with SPS with 20 ms, 50 ms, and 100 ms RRI respectively. In our fourth and final contribution, we look at the tracking error and age-of-information (AoI) of the latest 3GPP Release 16 NR-V2X standard, which includes enhancements to the 3GPP Release 14 C-V2X standard. The successor to Mode-4 C-V2X, known as Mode-2a NR-V2X, makes slight changes to sensing-based semi-persistent scheduling (SPS), though vehicles can still sense and schedule transmissions without a base station present. We use AoI and tracking error, which is the freshness of the information at the receiver and the difference in estimated vs actual location of a transmitting vehicle respectively, to measure the impact of lost and outdated BSMs on a vehicle's ability to localize neighboring vehicles. In this work, we again show that such BSM scheduling (with a fixed RRI) suffers from severe under- and over- utilization of radio resources, which severely compromises timely dissemination of BSMs and increases the system AoI and tracking error. To address this, we propose an RRI selection algorithm that measures the age or freshness of messages from neighboring vehicles to select an RRI, termed Age of Information (AoI)-aware RRI (AoI-RRI) selection. Specifically, AoI-aware SPS (i) measures the neighborhood AoI (as opposed to channel availability) to select an age-optimal RRI and (ii) uses a modified SPS procedure with the chosen RRI to select BSM transmission opportunities that minimize the overall system AoI. We compare AoI-RRI SPS to Ch-RRI SPS and fixed RRI SPS for NR-V2X. Our experiments based on the Mode-2a NR-V2X standard implemented using system level simulations show both Ch-RRI SPS and AoI-RRI SPS outperform SPS in high density scenarios in terms of tracking error and age-of-information. / Doctor of Philosophy / An increasing number of vehicles are equipped with a large set of on-board sensors that enable and support autonomous capabilities. Such sensors, which include Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), and cameras, are meant to increase passenger and driver safety. However, similar to humans, these sensors are limited to line-of-sight (LOS) visibility, meaning they cannot see beyond other vehicles, corners, and buildings. For this reason, efficient vehicular communications are essential to the next generation of vehicles and could significantly improve road safety. In addition, vehicular communications enable the timely exchange of critical information with other vehicles, cellular and roadside infrastructure, and pedestrians. However, unlike typical wireless and cellular networks, vehicular networks are expected to operate in a distributed manner, as there is no guarantee of the presence of cellular infrastructure. Accurate simulations and analytical models are critical in improving and guaranteeing the performance of the next generation of vehicular networks. In this dissertation, we propose and develop novel and practical distributed algorithms to enhance the performance of decentralized vehicular communications. We support these algorithms with computer simulations and analytical tools from the field of stochastic geometry.

In the context of wireless communications, low latency autonomous systems continue to grow in importance. Some applications of autonomous systems where low latency communication is essential are (i) vehicular network's safety performance depends on how recently the vehicles are updated on their neighboring vehicle's locations, (ii) updates from IoT devices need to be aggregated appropriately at the monitoring station before the information gets stale to extract temporal and spatial information from it, and (iii) sensors and controllers in a smart grid need to track the most recent state of the system to tune system parameters dynamically, etc. Each of the above-mentioned applications differs based on the connectivity between the source and the destination. First, vehicular networks involve a broadcast network where each of the vehicles broadcasts its packets to all the other vehicles. Secondly, in the case of UAV-assisted IoT networks, packets generated at multiple IoT devices are transmitted to a final destination via relays. Finally for the smart grid and generally for distributed systems, each source can have varying and unique destinations. Therefore in terms of connectivity, they can be categorized into one-to-all, all-to-one, and variable relationship between the number of sources and destinations. Additionally, some of the other major differences between the applications are the impact of mobility, the importance of a reduced AoI, centralized vs distributed manner of measuring AoI, etc. Thus the wide variety of application requirements makes it challenging to develop scheduling schemes that universally address minimizing the AoI. All these applications involve generating time-stamped status updates at a source which are then transmitted to their destination over a wireless medium. The timely reception of these updates at the destination decides the operating state of the system. This is because the fresher the information at the destination, the better its awareness of the system state for making better control decisions. This freshness of information is not the same as maximizing the throughput or minimizing the delay. While ideally throughput can be maximized by sending data as fast as possible, this may saturate the receiver resulting in queuing, contention, and other delays. On the other hand, these delays can be minimized by sending updates slowly, but this may cause high inter-arrival times. Therefore, a new metric called the Age of Information (AoI) has been proposed to measure the freshness of information that can account for many facets that influence data availability. In simple terms, AoI is measured at the destination as the time elapsed since the generation time of the most recently received update. Therefore AoI is able to incorporate both the delay and the inter-packet arrival time. This makes it a much better metric to measure end-to-end latency, and hence characterize the performance of such time-sensitive systems. These basic characteristics of AoI are explained in detail in Chapter 1. Overall, the main contribution of this dissertation is developing scheduling and resource allocation schemes targeted at improving the AoI of various autonomous systems having different types of connectivity, namely vehicular networks, UAV-assisted IoT networks, and smart grids, and then characterizing and quantifying the benefits of a reduced AoI from the application perspective. In the first contribution, we look into minimizing AoI for the case of broadcast networks having one-to-all connectivity between the source and destination devices by considering the case of vehicular networks. While vehicular networks have been studied in terms of AoI minimization, the impact of mobility and the benefit of a reduced AoI from the application perspective has not been investigated. The mobility of the vehicles is realistically modeled using the Simulation of Urban Mobility (SUMO) software to account for overtaking, lane changes, etc. We propose a safety metric that indicates the collision risk of a vehicle and do a simulation-based study on the ns3 simulator to study its relation to AoI. We see that the broadcast rate in a Dedicated Short Range Network (DSRC) that minimizes the system AoI also has the least collision risk, therefore signifying that reducing AoI improves the on-road safety of the vehicles. However, we also show that this relationship is not universally true and the mobility of the vehicles becomes a crucial aspect. Therefore, we propose a new metric called the Trackability-aware AoI (TAoI) which ensures that vehicles with unpredictable mobility broadcast at a faster rate while vehicles that are predicable are broadcasting at a reduced rate. The results obtained show that minimizing TAoI provides much better on-road safety as compared to plain AoI minimizing, which points to the importance of mobility in such applications. In the second contribution, we focus on networks with all-to-one connectivity where packets from multiple sources are transmitted to a single destination by taking an example of IoT networks. Here multiple IoT devices measure a physical phenomenon and transmit these measurements to a central base station (BS). However, under certain scenarios, the BS and IoT devices are unable to communicate directly and this necessitates the use of UAVs as relays. This creates a two-hop scenario that has not been studied for AoI minimization in UAV networks. In the first hop, the packets have to be sampled from the IoT devices to the UAV and then updated from the UAVs to the BS in the second hop. Such networks are called UAV-assisted IoT networks. We show that under ideal conditions with a generate-at-will traffic generation model and lossless wireless channels, the Maximal Age Difference (MAD) scheduler is the optimal AoI minimizing scheduler. When the ideal conditions are not applicable and more practical conditions are considered, a reinforcement learning (RL) based scheduler is desirable that can account for packet generation patterns and channel qualities. Therefore we propose to use a Deep-Q-Network (DQN)-based scheduler and it outperforms MAD and all other schedulers under general conditions. However, the DQN-based scheduler suffers from scalability issues in large networks. Therefore, another type of RL algorithm called Proximal Policy Optimization (PPO) is proposed to be used for larger networks. Additionally, the PPO-based scheduler can account for changes in the network conditions which the DQN-based scheduler was not able to do. This ensures the trained model can be deployed in environments that might be different than the trained environment. In the final contribution, AoI is studied in networks with varying connectivity between the source and destination devices. A typical example of such a distributed network is the smart grid where multiple devices exchange state information to ensure the grid operates in a stable state. To investigate AoI minimization and its impact on the smart grid, a co-simulation platform is designed where the 5G network is modeled in Python and the smart grid is modeled in PSCAD/MATLAB. In the first part of the study, the suitability of 5G in supporting smart grid operations is investigated. Based on the encouraging results that 5G can support a smart grid, we focus on the schedulers at the 5G RAN to minimize the AoI. It is seen that the AoI-based schedulers provide much better stability compared to traditional 5G schedulers like the proportional fairness and round-robin. However, the MAD scheduler which has been shown to be optimal for a variety of scenarios is no longer optimal as it cannot account for the connectivity among the devices. Additionally, distributed networks with heterogeneous sources will, in addition to the varying connectivity, have different sized packets requiring a different number of resource blocks (RB) to transmit, packet generation patterns, channel conditions, etc. This motivates an RL-based approach. Hence we propose a DQN-based scheduler that can take these factors into account and results show that the DQN-based scheduler outperforms all other schedulers in all considered conditions. / Doctor of Philosophy / Age of information (AoI) is an exciting new metric as it is able to characterize the freshness of information, where freshness means how representative the information is of the current system state. Therefore it is being actively investigated for a variety of autonomous systems that rely on having the most up-to-date information on the current state. Some examples are vehicular networks, UAV networks, and smart grids. Vehicular networks need the real-time location of their neighbor vehicles to make maneuver decisions, UAVs have to collect the most recent information from IoT devices for monitoring purposes, and devices in a smart grid need to ensure that they have the most recent information on the desired system state. From a communication point of view, each of these scenarios presents a different type of connectivity between the source and the destination. First, the vehicular network is a broadcast network where each vehicle broadcasts its packets to every other vehicle. Secondly, in the UAV network, multiple devices transmit their packets to a single destination via a relay. Finally, with the smart grid and the generally distributed networks, every source can have different and unique destinations. In these applications, AoI becomes a natural choice to measure the system performance as the fresher the information at the destination, the better its awareness of the system state which allows it to take better control decisions to reach the desired objective. Therefore in this dissertation, we use mathematical analysis and simulation-based approaches to investigate different scheduling and resource allocation policies to improve the AoI for the above-mentioned scenarios. We also show that the reduced AoI improves the system performance, i.e., better on-road safety for vehicular networks and better stability for smart grid applications. The results obtained in this dissertation show that when designing communication and networking protocols for time-sensitive applications requiring low latency, they have to be optimized to improve AoI. This is in contrast to most modern-day communication protocols that are targeted at improving the throughput or minimizing the delay.

Vehicle-related communications are a key application to be enabled by Fifth Generation (5G) wireless systems. The communications enabled by the future Internet of Vehicles (IoV) that are connected to every wireless device are referred to as Vehicle-to-Everything (V2X) communications. A major application of V2X communication systems will be to provide emergency warnings. This thesis evaluates Long-Term Evolution (LTE) and Dedicated Short Range Communications (DSRC) in terms of service quality and latency, and provides guidelines for design of cooperative LTE-DSRC systems for V2X communications. An extensive simulation analysis shows that (1) the number of users in need of warning has an effect on latency, and more so for LTE than for DSRC, (2) the DSRC priority parameter has an impact on the latency, and (3) wider system bandwidths and smaller cell sizes reduce latency for LTE. The end-to-end latency of LTE can be as high as 1.3 s, whereas the DSRC latency is below 15 ms for up to 250 users. Also, improving performance of systems is as much as important as studying about latency. One method to improving performance is using a better suitable antenna for physical communication. The mobility of vehicles results in a highly variable propagation channel that complicates communication. Use of a smart, steerable antenna can be one solution. The most commonly used antennas for vehicular communication are omnidirectional. Such antennas have consistent performance over all angles in the horizontal plane; however, rapidly steerable directional antennas should perform better in a dynamic propagation environment. A linear array antenna can perform dynamical appropriate azimuth pattern by having different weights of each element. The later section includes (1) identifying beam pattern parameters based on locations of a vehicular transmitter and fixed receivers and (2) an approach to find weights of each element of linear array antenna. Through the simulations with our approach and realistic scenarios, the desired array pattern can be achieved and array element weights can be calculated for the desired beam pattern. Based on the simulation results, DSRC is preferred to use in the scenario which contains large number of users with setup of higher priority, and LTE is preferred to use with wider bandwidth and smaller cell size. Also, the approach to find the controllable array antenna can be developed to the actual implementation of hardware with USRP. / Master of Science

Over the last few decades, wireless networks have morphed from traditional cellular/wireless local area networks (WLAN), into a wide range of applications, such as the Internet-of-Things (IoT), vehicular-to-everything (V2X), and smart grid communication networks. This transition has been facilitated by research and development efforts in academia and industry, which has resulted in the standardization of fifth-generation (5G) wireless networks. To meet the performance requirements of these diverse use-cases, 5G networks demand higher performance in terms of data rate, latency, security, and reliability, etc. At the physical layer, these performance enhancements are achieved by (a) optimizing spectrum utilization shared amongst multiple technologies (termed as spectrum sharing), and (b) leveraging advanced spatial signal processing techniques using large antenna arrays (termed as massive MIMO). In this dissertation, we focus on enhancing the performance of next-generation vehicular communication and spectrum sharing systems. In the first contribution, we present a novel pilot configuration design and adaptation mechanism for cellular vehicular-to-everything (C-V2X) networks. Drawing inspiration from 4G and 5G standards, the proposed approach is based on limited feedback of indices from a codebook comprised of quantized channel statistics information. We demonstrate significant rate improvements using our proposed approach in terrestrial and air-to-ground (A2G) vehicular channels. In the second contribution, we demonstrate the occurrence of cellular link adaptation failure due to channel state information (CSI) contamination, because of coexisting pulsed radar signals that act as non-pilot interference. To mitigate this problem, we propose a low-complexity semi-blind SINR estimation scheme that is robust and accurate in a wide range of interference and noise conditions. We also propose a novel dual CSI feedback mechanism for cellular systems and demonstrate significant improvements in throughput, block error rate, and latency, when sharing spectrum with a pulsed radar. In the third contribution, we develop fundamental insights on underlay radar-massive MIMO spectrum sharing, using mathematical tools from stochastic geometry. We consider a multi-antenna radar system, sharing spectrum with a network of massive MIMO base stations distributed as a homogeneous Poisson Point Process (PPP) outside a circular exclusion zone centered around the radar. We propose a tractable analytical framework, and characterize the impact of worst-case downlink cellular interference on radar performance, as a function of key system parameters. The analytical formulation enables network designers to systematically isolate and evaluate the impact of each parameter on the worst-case radar performance and complements industry-standard simulation methodologies by establishing a baseline performance for each set of system parameters, for current and future radar-cellular spectrum sharing deployments. Finally, we highlight directions for future work to advance the research presented in this dissertation and discuss its broader impacts across the wireless industry, and policy-making. / Doctor of Philosophy / The impact of today's technologies has been magnified by wireless networks, due to the standardization and deployment of fifth-generation (5G) cellular networks. 5G promises faster data speeds, lower latency and higher user security, among other desirable features. This has made it capable of meeting the performance requirements of key infrastructure such as smart grid and mission-critical networks, and novel consumer applications such as smart home appliances, smart vehicles, and augmented/virtual reality. In part, these capabilities have been achieved by (a) better spectrum utilization among various wireless technologies (called spectrum sharing), and (b) serving multiple users on the same resource using large multi-antenna systems (called massive MIMO). In this dissertation, we make three contributions that enhance the performance of vehicular communications and spectrum sharing systems. In the first contribution, we present a novel scheme wherein a vehicular communication link adapts to the channel conditions by controlling the resource overhead in real-time, to improve spectral utilization of data resources. The proposed scheme enhances those of current 4G and 5G networks, which are based on limited feedback of quantized channel statistics, fed back from the receiver to the transmitter. In the second contribution, we show that conventional link adaptation methods fail when 4G/5G networks share spectrum with pulsed radars. To mitigate this problem, we develop a comprehensive signal processing framework, consisting of a hybrid SINR estimation method that is robust and accurate in a wide range of interference and noise conditions. Concurrently, we also propose a scheme to pass additional information that captures the channel conditions in the presence of radar interference, and analyze its performance in detail. In the third contribution, we focus on characterizing the impact of 5G cellular interference on a radar system in shared spectrum, using mathematical tools from stochastic geometry. We model the worst-case interference scenario, and study the impact of the system parameters on the worst-case radar performance. In summary, this dissertation advances the state-of-the-art in vehicular communications and spectrum sharing, through (a) novel contributions in protocol design and (b) development of mathematical tools for performance characterization.

Vehicular communication systems are integral for efficient highway operational management and for mitigating severe traffic congestion. While vehicular ad hoc networks (VANET) are reliable and provide avenues to minimal reliance on existing infrastructure, they can experience high communication overhead and network disruptions. Vehicular micro clouds (VMCs) provide a promising solution to overcome the challenges of VANET by reducing communication latency through cooperative and collaborative resource allocation and data offloading. This thesis offers a comparative performance analysis of freeway incident management and vehicle platooning, comparing VANET communications versus stationary and platoon-based dynamic VMCs. Specifically, it studies speed and lane-changing advisories in addition to freeway platooning. To further enhance the analysis, the performance of both communication architectures is evaluated using communication protocols of DSRC versus cellular technologies of C-V2X, 4G LTE, and 5G NR for latency, bandwidth, range, and deployment considerations. The system-level features, such as driving safety and vehicular mobility are measured to evaluate the efficacy of the communication systems under incident-induced traffic conditions. The study uses the AIMSUN microscopic traffic simulator to model and analyze the performance of the proposed systems. Key performance indicators include communication latency and packet loss ratio. In addition, the stationary and dynamic cloud systems show advantages in reducing travel time delay, even at high penetration rates of the connected vehicles, whilst reducing collision risks. On average, we observe improvements in travel time by 10% by implementing vehicular clouds over traditional ad-hoc networks. From a communications standpoint, the overall latency delay and packet loss are reduced by 7% and 11%, respectively, with the implementation of cloud models. The findings also delineate the benefits of dynamic cloud models, given their improved manoeuvrability, can maximize the computational capabilities of CVs, even at high market penetrations in large-scale freeway demands. The results suggest a shift towards more reliance on connected vehicular clouds to minimize the risks associated with message interference and system overload, whilst fostering advancements in intelligent freeway traffic management systems. / Thesis / Master of Applied Science (MASc)

This work presents the development of the Statistical Location-Assisted Broadcast (SLAB) protocol, a multi-hop wireless broadcast protocol designed for vehicular ad-hoc networking (VANET). Vehicular networking is an important emerging application of wireless communications. Data dissemination applications using VANET promote the ability for vehicles to share information with each other and the wide-area network with the goal of improving navigation, fuel consumption, public safety, and entertainment. A critical component of these data dissemination schemes is the multi-hop wireless broadcast protocol. Multi-hop broadcast protocols for these schemes must reliably deliver broadcast packets to vehicles in a geographically bounded region while consuming as little wireless bandwidth as possible. This work contains substantial research results related to development of multi-hop broadcast protocols for VANET, culminating in the design of SLAB. Many preliminary research and development efforts have been required to arrive at SLAB. First, a high-level wireless broadcast simulation tool called WiBDAT is developed. Next, a manual optimization procedure is proposed to create efficient threshold functions for statistical broadcast protocols. This procedure is then employed to design the Distribution-Adaptive Distance with Channel Quality (DADCQ) broadcast protocol, a preliminary cousin of SLAB. DADCQ is highly adaptive to node density, node spatial distribution pattern, and wireless channel quality in realistic VANET scenarios. However, the manual design process used to create DADCQ has a few deficiencies. In response to these problems, an automated design procedure is created that uses a black-box global optimization algorithm to search for efficient threshold functions that are evaluated using WiBDAT. SLAB is finally designed using this procedure. / Expansive simulation results are presented comparing the performance of SLAB to two well-published VANET broadcast protocols, p -persistence and Advanced Adaptive Gossip (AAG), and to DADCQ. The four protocols are evaluated under varying node density and speed on five different road topologies with varying wireless channel fading conditions. The results demonstrate that unlike p-persistence and AAG, SLAB performs well across a very broad range of environmental conditions. Compared to its cousin protocol DADCQ, SLAB achieves similar reachability while using around 30% less wireless bandwidth, highlighting the improvement in the automated design methodology over the manual design. / by Michael J. Slavik. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 200?. Mode of access: World Wide Web.

Ferramentas computacionais, baseadas em modelos de inventários, que consigam predizer, com precisão apropriada, o consumo de combustíveis e as emissões veiculares, principais motivadores do desenvolvimento automotivo atual, são fundamentais para a elaboração de políticas públicas eficazes vinculadas a essas questões. No Brasil, apesar de afetarem significativamente a sociedade, muitas das intervenções governamentais nesse segmento são realizadas sem a adequada avaliação de seus impactos. Isso ocorre ou porque a importância dessas ferramentas nem sempre é reconhecida ou por não se ter ferramentas apropriadas. O objetivo deste trabalho foi o de desenvolver uma ferramenta de prognóstico do consumo de combustíveis e das emissões da frota rodoviária, estatisticamente consistente, que pudesse ser utilizada para esse propósito. Para tanto, inicialmente buscou-se identificar os aspectos relacionados às questões que deveriam ser considerados nesse desenvolvimento, por meio do levantamento das tendências evolutivas e alternativas que estão sendo apresentadas relativas a combustíveis, tecnologia veicular e sistemas de transportes. A seguir foram revistos os principais modelos e ferramentas públicos, nacionais e estrangeiros, dessa natureza. E, diante da constatação que eles não poderiam ser empregados, apesar dos limites impostos pela disponibilidade de dados, foi proposta uma nova abordagem para se atingir esse objetivo. Primeiro foi desenvolvido um conjunto de planilhas integrando todos os dados e cálculos de um modelo bottom-up similar ao utilizado nos inventários de emissões tóxicas nacionais, totalmente interconectado e configurado para facilitar, por meio de um processo iterativo, o ajuste fino das estimativas mais incertas, de forma que os consumos totalizados resultantes do modelo coincidissem tanto quanto possível com os consumos observados no país. A seguir, foram desenvolvidos modelos econométricos, para estimar os consumos totalizados de combustíveis a partir de indicadores econômicos pertinentes. E pela inserção desses modelos econométricos no modelo bottom-up anterior, foi gerado um modelo híbrido que permite inventariar e prognosticar, segregadas por classes, o consumo de combustíveis e as emissões veiculares. Os consumos de combustíveis obtidos por meio desses modelos, quando comparados com valores observados, forneceram resultados estatisticamente robustos, que podem ser, em algumas condições, convertidos na emissão de CO2. O mesmo não é possível afirmar com relação às demais emissões, inclusive em função da dificuldade de vincular as fontes emissoras a indicadores de qualidade do ar, o que não invalida o uso do modelo híbrido para obtenção de resultados comparativos. Finalmente, os modelos foram submetidos a análise sensibilidade e sua aplicabilidade foi verificada para alguns cenários. / Computational tools, based on inventory models, which are able to predict, with the appropriated accuracy, vehicular fuel consumption and emissions, main current drivers of the automotive development, are essentials for the development of effective public policies related to these issues. In Brazil, despite their significantly influence over the society, many government interventions in this segment are undertaken without the adequate assessment of their impacts. This happen because the importance of these tools is not always recognized or because proper tools are not available. The goal of this study was to develop a statistically consistent prognostic tool of road fuel consumption and emissions, which could be used for this purpose. Initially, by surveying the evolutionary trends and known alternatives related to fuels, vehicular technology and transportation systems, it was examined all the aspects that should be considered for this work. Next it was reviewed the main domestic and foreign public models and tools of this sector. Realizing that they could not be used, due to the limits imposed by data availability, it is proposed a new approach to achieve this goal. First it is developed a set of worksheets integrating all data and calculations of a bottom-up model, similar to the ones used in national inventories of toxic emissions. The set was interconnected and configured to facilitate, through an iterative process, the fine tuning of the uncertain estimates, in such way that the model total consumptions as much as possible reproduce the fuel consumptions observed in the country. Next econometric models were developed to estimate total fuel consumptions based on identified relevant economic indicators. And inserting these econometric models in the previous bottom-up model, it was generated a hybrid model that allows inventorying and forecasting of fuel consumptions and vehicular emissions segregated by classes. When compared with the observed fuel consumptions, these models presented statistically robust results. Under some conditions, these results can be converted in CO2 emissions. The same cannot be said with respect to other emissions, partially due to the difficulty to link emission sources to air quality measurements, which does not invalidate the use of the hybrid model to obtain comparative results for these emissions. Finally, the models were submitted to a sensitivity analysis and their applicability was verified for some scenarios.