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- All Subjects: Computer Engineering
Description
As the complexity of robotic systems and applications grows rapidly, development of high-performance, easy to use, and fully integrated development environments for those systems is inevitable. Model-Based Design (MBD) of dynamic systems using engineering software such as Simulink® from MathWorks®, SciCos from Metalau team and SystemModeler® from Wolfram® is quite popular nowadays. They provide tools for modeling, simulation, verification and in some cases automatic code generation for desktop applications, embedded systems and robots. For real-world implementation of models on the actual hardware, those models should be converted into compilable machine code either manually or automatically. Due to the complexity of robotic systems, manual code translation from model to code is not a feasible optimal solution so we need to move towards automated code generation for such systems. MathWorks® offers code generation facilities called Coder® products for this purpose. However in order to fully exploit the power of model-based design and code generation tools for robotic applications, we need to enhance those software systems by adding and modifying toolboxes, files and other artifacts as well as developing guidelines and procedures. In this thesis, an effort has been made to propose a guideline as well as a Simulink® library, StateFlow® interface API and a C/C++ interface API to complete this toolchain for NAO humanoid robots. Thus the model of the hierarchical control architecture can be easily and properly converted to code and built for implementation.
ContributorsRaji Kermani, Ramtin (Author) / Fainekos, Georgios (Thesis advisor) / Lee, Yann-Hang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis introduces the Model-Based Development of Multi-iRobot Toolbox (MBDMIRT), a Simulink-based toolbox designed to provide the means to acquire and practice the Model-Based Development (MBD) skills necessary to design real-time embedded system. The toolbox was developed in the Cyber-Physical System Laboratory at Arizona State University. The MBDMIRT toolbox runs under MATLAB/Simulink to simulate the movements of multiple iRobots and to control, after verification by simulation, multiple physical iRobots accordingly. It adopts the Simulink/Stateflow, which exemplifies an approach to MBD, to program the behaviors of the iRobots. The MBDMIRT toolbox reuses and augments the open-source MATLAB-Based Simulator for the iRobot Create from Cornell University to run the simulation. Regarding the mechanism of iRobot control, the MBDMIRT toolbox applies the MATLAB Toolbox for the iRobot Create (MTIC) from United States Naval Academy to command the physical iRobots. The MBDMIRT toolbox supports a timer in both the simulation and the control, which is based on the local clock of the PC running the toolbox. In addition to the build-in sensors of an iRobot, the toolbox can simulate four user-added sensors, which are overhead localization system (OLS), sonar sensors, a camera, and Light Detection And Ranging (LIDAR). While controlling a physical iRobot, the toolbox supports the StarGazer OLS manufactured by HAGISONIC, Inc.
ContributorsSu, Shih-Kai (Author) / Fainekos, Georgios E (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Artemiadis, Panagiotis K (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation considers the question of how convenient access to copious networked observational data impacts our ability to learn causal knowledge. It investigates in what ways learning causality from such data is different from -- or the same as -- the traditional causal inference which often deals with small scale i.i.d. data collected from randomized controlled trials? For example, how can we exploit network information for a series of tasks in the area of learning causality? To answer this question, the dissertation is written toward developing a suite of novel causal learning algorithms that offer actionable insights for a series of causal inference tasks with networked observational data. The work aims to benefit real-world decision-making across a variety of highly influential applications. In the first part of this dissertation, it investigates the task of inferring individual-level causal effects from networked observational data. First, it presents a representation balancing-based framework for handling the influence of hidden confounders to achieve accurate estimates of causal effects. Second, it extends the framework with an adversarial learning approach to properly combine two types of existing heuristics: representation balancing and treatment prediction. The second part of the dissertation describes a framework for counterfactual evaluation of treatment assignment policies with networked observational data. A novel framework that captures patterns of hidden confounders is developed to provide more informative input for downstream counterfactual evaluation methods. The third part presents a framework for debiasing two-dimensional grid-based e-commerce search with observational search log data where there is an implicit network connecting neighboring products in a search result page. A novel inverse propensity scoring framework that models user behavior patterns for two-dimensional display in e-commerce websites is developed, which aims to optimize online performance of ranking algorithms with offline log data.
ContributorsGuo, Ruocheng (Author) / Liu, Huan (Thesis advisor) / Candan, K. Selcuk (Committee member) / Xue, Guoliang (Committee member) / Kiciman, Emre (Committee member) / Arizona State University (Publisher)
Created2021
Description
The Internet-of-Things (IoT) paradigm is reshaping the ways to interact with the physical space. Many emerging IoT applications need to acquire, process, gain insights from, and act upon the massive amount of data continuously produced by ubiquitous IoT sensors. It is nevertheless technically challenging and economically prohibitive for each IoT application to deploy and maintain a dedicated large-scale sensor network over distributed wide geographic areas. Built upon the Sensing-as-a-Service paradigm, cloud-sensing service providers are emerging to provide heterogeneous sensing data to various IoT applications with a shared sensing substrate. Cyber threats are among the biggest obstacles against the faster development of cloud-sensing services. This dissertation presents novel solutions to achieve trustworthy IoT sensing-as-a-service. Chapter 1 introduces the cloud-sensing system architecture and the outline of this dissertation. Chapter 2 presents MagAuth, a secure and usable two-factor authentication scheme that explores commercial off-the-shelf wrist wearables with magnetic strap bands to enhance the security and usability of password-based authentication for touchscreen IoT devices. Chapter 3 presents SmartMagnet, a novel scheme that combines smartphones and cheap magnets to achieve proximity-based access control for IoT devices. Chapter 4 proposes SpecKriging, a new spatial-interpolation technique based on graphic neural networks for secure cooperative spectrum sensing which is an important application of cloud-sensing systems. Chapter 5 proposes a trustworthy multi-transmitter localization scheme based on SpecKriging. Chapter 6 discusses the future work.
ContributorsZhang, Yan (Author) / Zhang, Yanchao YZ (Thesis advisor) / Fan, Deliang (Committee member) / Xue, Guoliang (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Data mining, also known as big data analysis, has been identified as a critical and challenging process for a variety of applications in real-world problems. Numerous datasets are collected and generated every day to store the information. The rise in the number of data volumes and data modality has resulted in the increased demand for data mining methods and strategies of finding anomalies, patterns, and correlations within large data sets to predict outcomes. Effective machine learning methods are widely adapted to build the data mining pipeline for various purposes like business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The major challenges for effectively and efficiently mining big data include (1) data heterogeneity and (2) missing data. Heterogeneity is the natural characteristic of big data, as the data is typically collected from different sources with diverse formats. The missing value is the most common issue faced by the heterogeneous data analysis, which resulted from variety of factors including the data collecting processing, user initiatives, erroneous data entries, and so on. In response to these challenges, in this thesis, three main research directions with application scenarios have been investigated: (1) Mining and Formulating Heterogeneous Data, (2) missing value imputation strategy in various application scenarios in both offline and online manner, and (3) missing value imputation for multi-modality data. Multiple strategies with theoretical analysis are presented, and the evaluation of the effectiveness of the proposed algorithms compared with state-of-the-art methods is discussed.
Contributorsliu, Xu (Author) / He, Jingrui (Thesis advisor) / Xue, Guoliang (Thesis advisor) / Li, Baoxin (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Modern data center networks require efficient and scalable security analysis approaches that can analyze the relationship between the vulnerabilities. Utilizing the Attack Representation Methods (ARMs) and Attack Graphs (AGs) enables the security administrator to understand the cloud network’s current security situation at the low-level. However, the AG approach suffers from scalability challenges. It relies on the connectivity between the services and the vulnerabilities associated with the services to allow the system administrator to realize its security state. In addition, the security policies created by the administrator can have conflicts among them, which is often detected in the data plane of the Software Defined Networking (SDN) system. Such conflicts can cause security breaches and increase the flow rules processing delay. This dissertation addresses these challenges with novel solutions to tackle the scalability issue of Attack Graphs and detect security policy conflictsin the application plane before they are transmitted into the data plane for final
installation. Specifically, it introduces a segmentation-based scalable security state
(S3) framework for the cloud network. This framework utilizes the well-known divide-and-conquer approach to divide the large network region into smaller, manageable segments. It follows a well-known segmentation approach derived from the K-means clustering algorithm to partition the system into segments based on the similarity between the services. Furthermore, the dissertation presents unified intent rules that abstract the network administration from the underlying network controller’s format. It develops a networking service solution to use a bounded formal model for network service compliance checking that significantly reduces the complexity of flow rule conflict checking at the data plane level. The solution can be expended from a single SDN domain to multiple SDN domains and hybrid networks by applying network service function chaining (SFC) for inter-domain policy management.
ContributorsSabur, Abdulhakim (Author) / Zhao, Ming (Thesis advisor) / Xue, Guoliang (Committee member) / Davulcu, Hasan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2023
Description
Internet of Things (IoT) is emerging as part of the infrastructures for advancing a large variety of applications involving connections of many intelligent devices, leading to smart communities. Due to the severe limitation of the computing resources of IoT devices, it is common to offload tasks of various applications requiring substantial computing resources to computing systems with sufficient computing resources, such as servers, cloud systems, and/or data centers for processing. However, this offloading method suffers from both high latency and network congestion in the IoT infrastructures.
Recently edge computing has emerged to reduce the negative impacts of tasks offloading to remote computing systems. As edge computing is in close proximity to IoT devices, it can reduce the latency of task offloading and reduce network congestion. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced loads among these devices. In order to effectively explore the potential of edge computing to support IoT applications, it is necessary to have efficient task management and load balancing in edge computing networks.
In this dissertation research, an approach is presented to periodically distributing tasks within the edge computing network while satisfying the quality-of-service (QoS) requirements of tasks. The QoS requirements include task completion deadline and security requirement. The approach aims to maximize the number of tasks that can be accommodated in the edge computing network, with consideration of tasks’ priorities. The goal is achieved through the joint optimization of the computing resource allocation and network bandwidth provisioning. Evaluation results show the improvement of the approach in increasing the number of tasks that can be accommodated in the edge computing network and the efficiency in resource utilization.
Recently edge computing has emerged to reduce the negative impacts of tasks offloading to remote computing systems. As edge computing is in close proximity to IoT devices, it can reduce the latency of task offloading and reduce network congestion. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced loads among these devices. In order to effectively explore the potential of edge computing to support IoT applications, it is necessary to have efficient task management and load balancing in edge computing networks.
In this dissertation research, an approach is presented to periodically distributing tasks within the edge computing network while satisfying the quality-of-service (QoS) requirements of tasks. The QoS requirements include task completion deadline and security requirement. The approach aims to maximize the number of tasks that can be accommodated in the edge computing network, with consideration of tasks’ priorities. The goal is achieved through the joint optimization of the computing resource allocation and network bandwidth provisioning. Evaluation results show the improvement of the approach in increasing the number of tasks that can be accommodated in the edge computing network and the efficiency in resource utilization.
ContributorsSong, Yaozhong (Author) / Yau, Sik-Sang (Thesis advisor) / Huang, Dijiang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2018
Description
Software-as-a-Service (SaaS) has received significant attention in recent years as major computer companies such as Google, Microsoft, Amazon, and Salesforce are adopting this new approach to develop software and systems. Cloud computing is a computing infrastructure to enable rapid delivery of computing resources as a utility in a dynamic, scalable, and virtualized manner. Computer Simulations are widely utilized to analyze the behaviors of software and test them before fully implementations. Simulation can further benefit SaaS application in a cost-effective way taking the advantages of cloud such as customizability, configurability and multi-tendency.
This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.
The following chapters are provided in the architecture:
Multi-Tenancy Simulation Software-as-a-Service.
Policy Specification for MTA simulation environment.
Model Driven PaaS Based SaaS modeling.
Dynamic analysis and dynamic calibration for timing analysis.
Event-driven Service-Oriented Simulation Framework.
LTBD: A Triage Solution for SaaS.
This research introduces Modeling, Simulation and Analysis for Software-as-Service in Cloud. The researches cover the following topics: service modeling, policy specification, code generation, dynamic simulation, timing, event and log analysis. Moreover, the framework integrates current advantages of cloud: configurability, Multi-Tenancy, scalability and recoverability.
The following chapters are provided in the architecture:
Multi-Tenancy Simulation Software-as-a-Service.
Policy Specification for MTA simulation environment.
Model Driven PaaS Based SaaS modeling.
Dynamic analysis and dynamic calibration for timing analysis.
Event-driven Service-Oriented Simulation Framework.
LTBD: A Triage Solution for SaaS.
ContributorsLi, Wu (Author) / Tsai, Wei-Tek (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Ye, Jieping (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2015
Description
Android is currently the most widely used mobile operating system. The permission model in Android governs the resource access privileges of applications. The permission model however is amenable to various attacks, including re-delegation attacks, background snooping attacks and disclosure of private information. This thesis is aimed at understanding, analyzing and performing forensics on application behavior. This research sheds light on several security aspects, including the use of inter-process communications (IPC) to perform permission re-delegation attacks.
Android permission system is more of app-driven rather than user controlled, which means it is the applications that specify their permission requirement and the only thing which the user can do is choose not to install a particular application based on the requirements. Given the all or nothing choice, users succumb to pressures and needs to accept permissions requested. This thesis proposes a couple of ways for providing the users finer grained control of application privileges. The same methods can be used to evade the Permission Re-delegation attack.
This thesis also proposes and implements a novel methodology in Android that can be used to control the access privileges of an Android application, taking into consideration the context of the running application. This application-context based permission usage is further used to analyze a set of sample applications. We found the evidence of applications spoofing or divulging user sensitive information such as location information, contact information, phone id and numbers, in the background. Such activities can be used to track users for a variety of privacy-intrusive purposes. We have developed implementations that minimize several forms of privacy leaks that are routinely done by stock applications.
Android permission system is more of app-driven rather than user controlled, which means it is the applications that specify their permission requirement and the only thing which the user can do is choose not to install a particular application based on the requirements. Given the all or nothing choice, users succumb to pressures and needs to accept permissions requested. This thesis proposes a couple of ways for providing the users finer grained control of application privileges. The same methods can be used to evade the Permission Re-delegation attack.
This thesis also proposes and implements a novel methodology in Android that can be used to control the access privileges of an Android application, taking into consideration the context of the running application. This application-context based permission usage is further used to analyze a set of sample applications. We found the evidence of applications spoofing or divulging user sensitive information such as location information, contact information, phone id and numbers, in the background. Such activities can be used to track users for a variety of privacy-intrusive purposes. We have developed implementations that minimize several forms of privacy leaks that are routinely done by stock applications.
ContributorsGollapudi, Narasimha Aditya (Author) / Dasgupta, Partha (Thesis advisor) / Xue, Guoliang (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2014
Description
Imagine that we have a piece of matter that can change its physical properties like its shape, density, conductivity, or color in a programmable fashion based on either user input or autonomous sensing. This is the vision behind what is commonly known as programmable matter. Envisioning systems of nano-sensors devices, programmable matter consists of systems of simple computational elements, called particles, that can establish and release bonds, compute, and can actively move in a self-organized way. In this dissertation the feasibility of solving fundamental problems relevant for programmable matter is investigated. As a model for such self-organizing particle systems (SOPS), the geometric amoebot model is introduced. In this model, particles only have local information and have modest computational power. They achieve locomotion by expanding and contracting, which resembles the behavior of amoeba. Under this model, efficient local-control algorithms for the leader election problem in SOPS are presented. As a central problem for programmable matter, shape formation problems are then studied. The limitations of solving the leader election problem and the shape formation problem on a more general version of the amoebot model are also discussed. The \smart paint" problem is also studied which aims at having the particles self-organize in order to uniformly coat the surface of an object of arbitrary shape and size, forming multiple coating layers if necessary. A Universal Coating algorithm is presented and shown to be asymptotically worst-case optimal both in terms of time with high probability and work. In particular, the algorithm always terminates within a linear number of rounds with high probability. A linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information is presented, which implies that the proposed algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of the proposed algorithm may be better than linear in practice. Developed algorithms utilize only local control, require only constant-size memory particles, and are asymptotically optimal in terms of the total number of particle movements needed to reach the desired shape configuration.
ContributorsDerakhshandeh, Zahra (Author) / Richa, Andrea (Thesis advisor) / Sen, Arunabha (Thesis advisor) / Xue, Guoliang (Committee member) / Scheideler, Christian (Committee member) / Arizona State University (Publisher)
Created2017