<p> This work seeks to define methodologies and techniques to analyze automotive fleet-tracking big data and provide sample results that have implications to the real world. To perform this work, vehicle fleet-tracking data from Odyne and Via Plug-in Hybrid Electric Trucks collected by the Electric Power Research Institute (EPRI) was used. Both CAN-communication bus signals and GPS data were recorded off of these vehicles with a second-by-second data collection rate. Colorado State University (CSU) was responsible for analyzing this data after it had been collected by EPRI and producing results with application to the real world. </p><p> A list of potential research questions is presented and an initial feasibility assessment is performed to determine how these questions might be answered using vehicle fleet-tracking data. Later, a subset of these questions are analyzed and answered in detail using the EPRI dataset. </p><p> The methodologies, techniques, and software used for this data analysis are described in detail. An algorithm that summarizes second-by-second vehicle tracking data into a list of higher-level driving and charging events is presented and utility factor (UF) curves and other statistics of interest are generated from this summarized event data. </p><p> In addition, another algorithm was built on the driving event identification algorithm to discretize the driving event data into approximately 90-second drive intervals. This allows for a regression model to be fit onto the data. A correlation between ambient temperature and equivalent vehicle fuel economy (in miles per gallon) is presented for Odyne and it is similar to the trend seen in conventional vehicle fuel economy vs. ambient temperature. It is also shown how ambient temperature variations can influence the vehicle fuel economy and there is a discussion about how changes in HVAC use could influence the fuel economy results. </p><p> It is also demonstrated how variations in the data analysis methodology can influence the final results. This provides evidence that vehicle fleet-tracking data analysis methodologies need to be defined to ensure that the data analysis results are of the highest quality. The questions and assumptions behind the presented analysis results are examined and a list of future work to address potential concerns and unanswered questions about the data analysis process is presented. Hopefully, this future work list will be beneficial to future vehicle data analysis projects. </p><p> The importance of using real-world driving data is demonstrated by comparing fuel economy results from our real-world data to the fuel economy calculated by EPA drive cycles. Utility factor curves calculated from the real-world data are also compared to standard utility factor curves that are presented in the SAE J2841 specification. Both of these comparisons showed a difference in real-world driving data, demonstrating the potential utility of evaluating vehicle technologies using the real-world big data techniques presented in this work. </p><p> Overall, this work documents some of the data analysis techniques that can be used for analyzing vehicle fleet-tracking big data and demonstrates the impact of the analysis results in the real world. It also provides evidence that the data analysis methodologies used to analyze vehicle fleet-tracking data need to be better defined and evaluated in future work.</p>

<p> Cognitive radio emerges as a technology to realize the dynamic spectrum access by dynamically configuring its transmission parameters. In a cognitive radio network (CRN), there are two types of users: primary users (PUs) and secondary users (SUs). PUs are the licensed users or the traditional wireless users who can access a specific licensed spectrum band. SUs are the unlicensed users equipped with cognitive radios that can opportunistically use currently unoccupied channels to transmit, but have to vacate channels for the returning PUs, and then switch to other available channels for continuous transmissions. When two SUs want to establish a link, they have to meet on the same channel that must be available for both of them simultaneously. This process is called <i>rendezvous.</i> </p><p> Past research works on rendezvous only focused on designing the channel hopping sequence for the rendezvous process while ignoring some practical problems like rendezvous in wide-band CRNs, rendezvous without a predetermined sender and receiver, rendezvous considering directional antennas, and how to maximize the number of common available channels. In this dissertation, we propose five schemes to realize efficient rendezvous and spectrum management considering these practical problems under different scenarios. We first propose a rendezvous and communication framework for wide-band CRNs. Furthermore, we propose two efficient rendezvous schemes without predetermined sender and receiver. Moreover, we propose a rendezvous scheme specifically for SUs equipped with directional antennas. Last, we propose a power control protocol to maximize the number of common available channels. All of the proposed schemes can realize both efficient rendezvous and spectrum management with practical assumptions under different scenarios.</p>

<p> This thesis studies the issues of building and managing future Internet of Things (IoT) systems. IoT systems consist of distributed components with services for sensing, processing, and controlling through devices deployed in our living environment as part of the global cyber-physical ecosystem. </p><p> Systems with perpetually running IoT devices may use a lot of energy. One challenge is implementing good management policies for energy saving. In addition, a large scale of devices may be deployed in wide geographical areas through low bandwidth wireless communication networks. This brings the challenge of congfiuring a large number of duplicated applications with low latency in a scalable manner. Finally, intelligent IoT applications, such as occupancy prediction and activity recognition, depend on analyzing user and event patterns from historical data. In order to achieve real-time interaction between humans and things, reliable yet real-time analytic support should be included to leverage the interplay and complementary roles of edge and cloud computing. </p><p> In this dissertation, I address the above issues from the service oriented point of view. Service oriented architecture (SOA) provides the integration and management flexibility using the abstraction of services deployed on devices. We have designed the WuKong IoT middleware to facilitate connectivity, deployment, and run-time management of IoT applications. </p><p> For energy efficient mapping, this thesis presents an energy saving methodology for co- locating several services on the same physical device in order to reduce the computing and communication energy. In a multi-hop network, the service co-location problem is formulated as a quadratic programming problem. I propose a reduction method that reduces it to the integer programming problem. In a single hop network, the service co-location problem can be modeled as the Maximum Weighted Independent Set (MWIS) problem. I design algorithm to transform a service flow to a co-location graph. Then, known heuristic algorithms to find the maximum independent set, which is the basis for making service co-location decisions, are applied to the co-location graph. </p><p> For low latency scalable deployment, I propose a region-based hierarchical management structure. A congestion zone that covers multiple regions is identified. The problem of deploying a large number of copies of a flow-based program (FBP) in a congestion zone is modeled as a network traffic congestion problem. Then, the problem of mapping in a congestion zone is modeled as an Integer Quadratic Constrained Programming (IQCP) problem, which is proved to be a NP-hard problem. Given that, an approximation algorithm based on LP relaxation and an efficient service relocating heuristic algorithm are designed for reducing the computation complexity. For each congestion zone, the algorithm will perform global optimized mapping for multiple regions, and then request multiple deployment delegators for reprogramming individual devices. </p><p> Finally, with the growing adoption of IoT applications, dedicated and single-purpose devices are giving way to smart, adaptive devices with rich capabilities using a platform or API, collecting and analyzing data, and making their own decisions. To facilitate building intelligent applications in IoT, I have implemented the edge framework for supporting reliable streaming analytics on edge devices. In addition, a progression framework is built to achieve the self-management capability of applications in IoT. A progressive architecture and a programming paradigm for bridging the service oriented application with the power of big data on the cloud are designed in the framework. In this thesis, I present the detailed design of the progression framework, which incorporates the above features for building scalable management of IoT systems through a flexible middleware.</p>

Digitized by Kansas Correctional Industries

<p> With growing use of autonomous vehicles and similar systems, it is critical for those systems to be reliable, accurate, and error free. Sensor data are of vital importance for ensuring the fidelity of navigation and decision-making ability of autonomous systems. Several existing models have achieved accuracy in the sensor data, but they are all application specific and have limited applicability for future systems. </p><p> This paper proposes a method for reducing errors in sensor data through use of sensor fusion on the Kalman filter. The proposed model is intended to be versatile and to adapt to the needs of any robotic vehicle with only minor modifications. The model is a basic framework for normalizing the speed of autonomous robots. Moreover, it is capable of ensuring smooth operation of individual autonomous robots and facilitates co-operative applications. The model achieves a framework that is more reliable, accurate, and error free, compared to other existing models, thereby enabling its implementation on similar robotic applications. This model can be expanded for use in other applications with only minimal changes; it therefore promises to revolutionize the way that human beings use, interact with, and benefit from autonomous devices in day-to-day activities.</p><p>

<p> The increasing demand for and prevalence of distributed energy resources (DER) such as solar power, electric vehicles, and energy storage, present a unique set of challenges for integration into a legacy power grid, and accurate models of the low-voltage distribution systems are critical for accurate simulations of DER. Accurate labeling of the phase connections for each customer in a utility model is one area of grid topology that is known to have errors and has implications for the safety, efficiency, and hosting capacity of a distribution system. This research presents a methodology for the phase identification of customers solely using the advanced metering infrastructure (AMI) voltage timeseries. This thesis proposes to use Spectral Clustering, combined with a sliding window ensemble method for utilizing a long-term, time-series dataset that includes missing data, to group customers within a lateral by phase. These clustering phase predictions validate over 90% of the existing phase labels in the model and identify customers where the current phase labels are incorrect in this model. Within this dataset, this methodology produces consistent, high-quality results, verified by validating the clustering phase predictions with the underlying topology of the system, as well as selected examples verified using satellite and street view images publicly available in Google Earth. Further analysis of the results of the Spectral Clustering predictions are also shown to not only validate and improve the phase labels in the utility model, but also show potential in the detection of other types of errors in the topology of the model such as errors in the labeling of connections between customers and transformers, unlabeled residential solar power, unlabeled transformers, and locating customers with incomplete information in the model. These results indicate excellent potential for further development of this methodology as a tool for validating and improving existing utility models of the low-voltage side of the distribution system.</p><p>

Thesis (Ph. D.)--Syracuse University, 2009. / "Publication number: AAT 3381559."

Thesis (Ph. D.)--Syracuse University, 2009. / "Publication number: AAT 3381581."

Thesis (M.S. in Software Engineering)--Naval Postgraduate School, June 2003. / Thesis advisor(s): Mikhail Auguston, Christopher D. Miles. Includes bibliographical references (p. 59-61). Also available online.

<p> Compilers are indispensable tools to developers. We expect them to be correct. However, compiler correctness is very hard to be reasoned about. This can be partly explained by the daunting complexity of compilers. </p><p> In this dissertation, I will explain how we constructed a random program generator, Csmith, and used it to find hundreds of bugs in strong open source compilers such as the GNU Compiler Collection (GCC) and the LLVM Compiler Infrastructure (LLVM). The success of Csmith depends on its ability of being expressive and unambiguous at the same time. Csmith is composed of a code generator and a GTAV (Generation-Time Analysis and Validation) engine. They work interactively to produce expressive yet unambiguous random programs. The expressiveness of Csmith is attributed to the code generator, while the unambiguity is assured by GTAV. GTAV performs program analyses, such as points-to analysis and effect analysis, efficiently to avoid ambiguities caused by undefined behaviors or unspecified behaviors. </p><p> During our 4.25 years of testing, Csmith has found over 450 bugs in the GNU Compiler Collection (GCC) and the LLVM Compiler Infrastructure (LLVM). We analyzed the bugs by putting them into different categories, studying the root causes, finding their locations in compilers' source code, and evaluating their importance. We believe analysis results are useful to future random testers, as well as compiler writers/users.</p>