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- Genre: Doctoral Dissertation
Description
The commercial semiconductor industry is gearing up for 5G communications in the 28GHz and higher band. In order to maintain the same relative receiver sensitivity, a larger number of antenna elements are required; the larger number of antenna elements is, in turn, driving semiconductor development. The purpose of this paper is to introduce a new method of dividing wireless communication protocols (such as the 802.11a/b/g
and cellular UMTS MAC protocols) across multiple unreliable communication links using a new link layer communication model in concert with a smart antenna aperture design referred to as Vector Antenna. A vector antenna is a ‘smart’ antenna system and as any smart antenna aperture, the design inherently requires unique microwave component performance as well as Digital Signal Processing (DSP) capabilities. This performance and these capabilities are further enhanced with a patented wireless protocol stack capability.
and cellular UMTS MAC protocols) across multiple unreliable communication links using a new link layer communication model in concert with a smart antenna aperture design referred to as Vector Antenna. A vector antenna is a ‘smart’ antenna system and as any smart antenna aperture, the design inherently requires unique microwave component performance as well as Digital Signal Processing (DSP) capabilities. This performance and these capabilities are further enhanced with a patented wireless protocol stack capability.
ContributorsJames, Frank Lee (Author) / Reisslein, Martin (Thesis advisor) / Seeling, Patrick (Thesis advisor) / McGarry, Michael (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Environmental pollution has been one of the most challenging problems in modern society and more and more health issues are now linked to environmental pollution and especially, air pollution. Certain sensitive group like patients with asthma are highly influenced by the environmental air quality and knowledge of the daily air pollution exposure is of great importance for the management and prevention of asthma attack. Hence small form factor, real time, accurate, sensitive and easy to use portable devices for environmental monitoring are of great value.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
ContributorsDu, Zijian (Author) / Tao, Nongjian (Thesis advisor) / Goryll, Michael (Committee member) / Herckes, Pierre (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2019
Description
Emerging modular cable network architectures distribute some cable headend functions to remote nodes that are located close to the broadcast cable links reaching the cable modems (CMs) in the subscriber homes and businesses. In the Remote- PHY (R-PHY) architecture, a Remote PHY Device (RPD) conducts the physical layer processing for the analog cable transmissions, while the headend runs the DOCSIS medium access control (MAC) for the upstream transmissions of the distributed CMs over the shared cable link. In contrast, in the Remote MACPHY (R-MACPHY) ar- chitecture, a Remote MACPHY Device (RMD) conducts both the physical and MAC layer processing. The dissertation objective is to conduct a comprehensive perfor- mance comparison of the R-PHY and R-MACPHY architectures. Also, development of analytical delay models for the polling-based MAC with Gated bandwidth alloca- tion of Poisson traffic in the R-PHY and R-MACPHY architectures and conducting extensive simulations to assess the accuracy of the analytical model and to evaluate the delay-throughput performance of the R-PHY and R-MACPHY architectures for a wide range of deployment and operating scenarios. Performance evaluations ex- tend to the use of Ethernet Passive Optical Network (EPON) as transport network between remote nodes and headend. The results show that for long CIN distances above 100 miles, the R-MACPHY architecture achieves significantly shorter mean up- stream packet delays than the R-PHY architecture, especially for bursty traffic. The extensive comparative R-PHY and R-MACPHY comparative evaluation can serve as a basis for the planning of modular broadcast cable based access networks.
ContributorsAlharbi, Ziyad Ghazai (Author) / Reisslein, Martin (Thesis advisor) / Thyagaturu, Akhilesh (Committee member) / Zhang, Yanchao (Committee member) / McGarry, Michael (Committee member) / Arizona State University (Publisher)
Created2019
Description
Emerging from years of research and development, the Internet-of-Things (IoT) has finally paved its way into our daily lives. From smart home to Industry 4.0, IoT has been fundamentally transforming numerous domains with its unique superpower of interconnecting world-wide devices. However, the capability of IoT is largely constrained by the limited resources it can employ in various application scenarios, including computing power, network resource, dedicated hardware, etc. The situation is further exacerbated by the stringent quality-of-service (QoS) requirements of many IoT applications, such as delay, bandwidth, security, reliability, and more. This mismatch in resources and demands has greatly hindered the deployment and utilization of IoT services in many resource-intense and QoS-sensitive scenarios like autonomous driving and virtual reality.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
ContributorsYu, Ruozhou, Ph.D (Author) / Xue, Guoliang (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Mechanical properties of cells are important in maintaining physiological functions of biological systems. Quantitative measurement and analysis of mechanical properties can help understand cellular mechanics and its functional relevance and discover physical biomarkers for diseases monitoring and therapeutics.
This dissertation presents a work to develop optical methods for studying cell mechanics which encompasses four applications. Surface plasmon resonance microscopy based optical method has been applied to image intracellular motions and cell mechanical motion. This label-free technique enables ultrafast imaging with extremely high sensitivity in detecting cell deformation. The technique was first applied to study intracellular transportation. Organelle transportation process and displacement steps of motor protein can be tracked using this method. The second application is to study heterogeneous subcellular membrane displacement induced by membrane potential (de)polarization. The application can map the amplitude and direction of cell deformation. The electromechanical coupling of mammalian cells was also observed. The third application is for imaging electrical activity in single cells with sub-millisecond resolution. This technique can fast record actions potentials and also resolve the fast initiation and propagation of electromechanical signals within single neurons. Bright-field optical imaging approach has been applied to the mechanical wave visualization that associated with action potential in the fourth application. Neuron-to-neuron viability of membrane displacement was revealed and heterogeneous subcellular response was observed.
All these works shed light on the possibility of using optical approaches to study millisecond-scale and sub-nanometer-scale mechanical motions. These studies revealed ultrafast and ultra-small mechanical motions at the cellular level, including motor protein-driven motions and electromechanical coupled motions. The observations will help understand cell mechanics and its biological functions. These optical approaches will also become powerful tools for elucidating the interplay between biological and physical functions.
This dissertation presents a work to develop optical methods for studying cell mechanics which encompasses four applications. Surface plasmon resonance microscopy based optical method has been applied to image intracellular motions and cell mechanical motion. This label-free technique enables ultrafast imaging with extremely high sensitivity in detecting cell deformation. The technique was first applied to study intracellular transportation. Organelle transportation process and displacement steps of motor protein can be tracked using this method. The second application is to study heterogeneous subcellular membrane displacement induced by membrane potential (de)polarization. The application can map the amplitude and direction of cell deformation. The electromechanical coupling of mammalian cells was also observed. The third application is for imaging electrical activity in single cells with sub-millisecond resolution. This technique can fast record actions potentials and also resolve the fast initiation and propagation of electromechanical signals within single neurons. Bright-field optical imaging approach has been applied to the mechanical wave visualization that associated with action potential in the fourth application. Neuron-to-neuron viability of membrane displacement was revealed and heterogeneous subcellular response was observed.
All these works shed light on the possibility of using optical approaches to study millisecond-scale and sub-nanometer-scale mechanical motions. These studies revealed ultrafast and ultra-small mechanical motions at the cellular level, including motor protein-driven motions and electromechanical coupled motions. The observations will help understand cell mechanics and its biological functions. These optical approaches will also become powerful tools for elucidating the interplay between biological and physical functions.
ContributorsYang, Yunze (Author) / Tao, Nongjian (Thesis advisor) / Wang, Shaopeng (Committee member) / Goryll, Michael (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2016
Description
A Fiber-Wireless (FiWi) network integrates a passive optical network (PON) with wireless mesh networks (WMNs) to provide high speed backhaul via the PON while offering the flexibility and mobility of a WMN. Generally, increasing the size of a WMN leads to higher wireless interference and longer packet delays. The partitioning of a large WMN into several smaller WMN clusters, whereby each cluster is served by an Optical Network Unit (ONU) of the PON, is examined. Existing WMN throughput-delay analysis techniques considering the mean load of the nodes at a given hop distance from a gateway (ONU) are unsuitable for the heterogeneous nodal traffic loads arising from clustering. A simple analytical queuing model that considers the individual node loads to accurately characterize the throughput-delay performance of a clustered FiWi network is introduced. The accuracy of the model is verified through extensive simulations. It is found that with sufficient PON bandwidth, clustering substantially improves the FiWi network throughput-delay performance by employing the model to examine the impact of the number of clusters on the network throughput-delay performance. Different traffic models and network designs are also studied to improve the FiWi network performance.
ContributorsChen, Po-Yen (Author) / Reisslein, Martin (Thesis advisor) / Seeling, Patrick (Committee member) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
Microblogging services such as Twitter, Sina Weibo, and Tumblr have been emerging and deeply embedded into people's daily lives. Used by hundreds of millions of users to connect the people worldwide and share and access information in real-time, the microblogging service has also became the target of malicious attackers due to its massive user engagement and structural openness. Although existed, little is still known in the community about new types of vulnerabilities in current microblogging services which could be leveraged by the intelligence-evolving attackers, and more importantly, the corresponding defenses that could prevent both the users and the microblogging service providers from being attacked. This dissertation aims to uncover a number of challenging security and privacy issues in microblogging services and also propose corresponding defenses.
This dissertation makes fivefold contributions. The first part presents the social botnet, a group of collaborative social bots under the control of a single botmaster, demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation, and propose the corresponding countermeasures and evaluate their effectiveness. Inspired by Pagerank, the second part describes TrueTop, the first sybil-resilient system to find the top-K influential users in microblogging services with very accurate results and strong resilience to sybil attacks. TrueTop has been implemented to handle millions of nodes and 100 times more edges on commodity computers. The third and fourth part demonstrate that microblogging systems' structural openness and users' carelessness could disclose the later's sensitive information such as home city and age. LocInfer, a novel and lightweight system, is presented to uncover the majority of the users in any metropolitan area; the dissertation also proposes MAIF, a novel machine learning framework that leverages public content and interaction information in microblogging services to infer users' hidden ages. Finally, the dissertation proposes the first privacy-preserving social media publishing framework to let the microblogging service providers publish their data to any third-party without disclosing users' privacy and meanwhile meeting the data's commercial utilities. This dissertation sheds the light on the state-of-the-art security and privacy issues in the microblogging services.
This dissertation makes fivefold contributions. The first part presents the social botnet, a group of collaborative social bots under the control of a single botmaster, demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation, and propose the corresponding countermeasures and evaluate their effectiveness. Inspired by Pagerank, the second part describes TrueTop, the first sybil-resilient system to find the top-K influential users in microblogging services with very accurate results and strong resilience to sybil attacks. TrueTop has been implemented to handle millions of nodes and 100 times more edges on commodity computers. The third and fourth part demonstrate that microblogging systems' structural openness and users' carelessness could disclose the later's sensitive information such as home city and age. LocInfer, a novel and lightweight system, is presented to uncover the majority of the users in any metropolitan area; the dissertation also proposes MAIF, a novel machine learning framework that leverages public content and interaction information in microblogging services to infer users' hidden ages. Finally, the dissertation proposes the first privacy-preserving social media publishing framework to let the microblogging service providers publish their data to any third-party without disclosing users' privacy and meanwhile meeting the data's commercial utilities. This dissertation sheds the light on the state-of-the-art security and privacy issues in the microblogging services.
ContributorsZhang, Jinxue (Author) / Zhang, Yanchao (Thesis advisor) / Zhang, Junshan (Committee member) / Ying, Lei (Committee member) / Ahn, Gail-Joon (Committee member) / Arizona State University (Publisher)
Created2016
Description
The recent years have witnessed a rapid development of mobile devices and smart devices. As more and more people are getting involved in the online environment, privacy issues are becoming increasingly important. People’s privacy in the digital world is much easier to leak than in the real world, because every action people take online would leave a trail of information which could be recorded, collected and used by malicious attackers. Besides, service providers might collect users’ information and analyze them, which also leads to a privacy breach. Therefore, preserving people’s privacy is very important in the online environment.
In this dissertation, I study the problems of preserving people’s identity privacy and loca- tion privacy in the online environment. Specifically, I study four topics: identity privacy in online social networks (OSNs), identity privacy in anonymous message submission, lo- cation privacy in location based social networks (LBSNs), and location privacy in location based reminders. In the first topic, I propose a system which can hide users’ identity and data from untrusted storage site where the OSN provider puts users’ data. I also design a fine grained access control mechanism which prevents unauthorized users from accessing the data. Based on the secret sharing scheme, I construct a shuffle protocol that disconnects the relationship between members’ identities and their submitted messages in the topic of identity privacy in anonymous message submission. The message is encrypted on the mem- ber side and decrypted on the message collector side. The collector eventually gets all of the messages but does not know who submitted which message. In the third topic, I pro- pose a framework that hides users’ check-in information from the LBSN. Considering the limited computation resources on smart devices, I propose a delegatable pseudo random function to outsource computations to the much more powerful server while preserving privacy. I also implement efficient revocations. In the topic of location privacy in location based reminders, I propose a system to hide users’ reminder locations from an untrusted cloud server. I propose a cross based approach and an improved bar based approach, re- spectively, to represent a reminder area. The reminder location and reminder message are encrypted before uploading to the cloud server, which then can determine whether the dis- tance between the user’s current location and the reminder location is within the reminder distance without knowing anything about the user’s location information and the content of the reminder message.
In this dissertation, I study the problems of preserving people’s identity privacy and loca- tion privacy in the online environment. Specifically, I study four topics: identity privacy in online social networks (OSNs), identity privacy in anonymous message submission, lo- cation privacy in location based social networks (LBSNs), and location privacy in location based reminders. In the first topic, I propose a system which can hide users’ identity and data from untrusted storage site where the OSN provider puts users’ data. I also design a fine grained access control mechanism which prevents unauthorized users from accessing the data. Based on the secret sharing scheme, I construct a shuffle protocol that disconnects the relationship between members’ identities and their submitted messages in the topic of identity privacy in anonymous message submission. The message is encrypted on the mem- ber side and decrypted on the message collector side. The collector eventually gets all of the messages but does not know who submitted which message. In the third topic, I pro- pose a framework that hides users’ check-in information from the LBSN. Considering the limited computation resources on smart devices, I propose a delegatable pseudo random function to outsource computations to the much more powerful server while preserving privacy. I also implement efficient revocations. In the topic of location privacy in location based reminders, I propose a system to hide users’ reminder locations from an untrusted cloud server. I propose a cross based approach and an improved bar based approach, re- spectively, to represent a reminder area. The reminder location and reminder message are encrypted before uploading to the cloud server, which then can determine whether the dis- tance between the user’s current location and the reminder location is within the reminder distance without knowing anything about the user’s location information and the content of the reminder message.
ContributorsZhao, Xinxin (Author) / Xue, Guoliang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Huang, Dijiang (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cloud computing is known as a new and powerful computing paradigm. This new generation of network computing model delivers both software and hardware as on-demand resources and various services over the Internet. However, the security concerns prevent users from adopting the cloud-based solutions to fulfill the IT requirement for many business critical computing. Due to the resource-sharing and multi-tenant nature of cloud-based solutions, cloud security is especially the most concern in the Infrastructure as a Service (IaaS). It has been attracting a lot of research and development effort in the past few years.
Virtualization is the main technology of cloud computing to enable multi-tenancy.
Computing power, storage, and network are all virtualizable to be shared in an IaaS system. This important technology makes abstract infrastructure and resources available to users as isolated virtual machines (VMs) and virtual networks (VNs). However, it also increases vulnerabilities and possible attack surfaces in the system, since all users in a cloud share these resources with others or even the attackers. The promising protection mechanism is required to ensure strong isolation, mediated sharing, and secure communications between VMs. Technologies for detecting anomalous traffic and protecting normal traffic in VNs are also needed. Therefore, how to secure and protect the private traffic in VNs and how to prevent the malicious traffic from shared resources are major security research challenges in a cloud system.
This dissertation proposes four novel frameworks to address challenges mentioned above. The first work is a new multi-phase distributed vulnerability, measurement, and countermeasure selection mechanism based on the attack graph analytical model. The second work is a hybrid intrusion detection and prevention system to protect VN and VM using virtual machines introspection (VMI) and software defined networking (SDN) technologies. The third work further improves the previous works by introducing a VM profiler and VM Security Index (VSI) to keep track the security status of each VM and suggest the optimal countermeasure to mitigate potential threats. The final work is a SDN-based proactive defense mechanism for a cloud system using a reconfiguration model and moving target defense approaches to actively and dynamically change the virtual network configuration of a cloud system.
Virtualization is the main technology of cloud computing to enable multi-tenancy.
Computing power, storage, and network are all virtualizable to be shared in an IaaS system. This important technology makes abstract infrastructure and resources available to users as isolated virtual machines (VMs) and virtual networks (VNs). However, it also increases vulnerabilities and possible attack surfaces in the system, since all users in a cloud share these resources with others or even the attackers. The promising protection mechanism is required to ensure strong isolation, mediated sharing, and secure communications between VMs. Technologies for detecting anomalous traffic and protecting normal traffic in VNs are also needed. Therefore, how to secure and protect the private traffic in VNs and how to prevent the malicious traffic from shared resources are major security research challenges in a cloud system.
This dissertation proposes four novel frameworks to address challenges mentioned above. The first work is a new multi-phase distributed vulnerability, measurement, and countermeasure selection mechanism based on the attack graph analytical model. The second work is a hybrid intrusion detection and prevention system to protect VN and VM using virtual machines introspection (VMI) and software defined networking (SDN) technologies. The third work further improves the previous works by introducing a VM profiler and VM Security Index (VSI) to keep track the security status of each VM and suggest the optimal countermeasure to mitigate potential threats. The final work is a SDN-based proactive defense mechanism for a cloud system using a reconfiguration model and moving target defense approaches to actively and dynamically change the virtual network configuration of a cloud system.
ContributorsChung, Chun-Jen (Author) / Huang, Dijiang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Xue, Guoliang (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
Access Networks provide the backbone to the Internet connecting the end-users to
the core network thus forming the most important segment for connectivity. Access
Networks have multiple physical layer medium ranging from fiber cables, to DSL links
and Wireless nodes, creating practically-used hybrid access networks. We explore the
hybrid access network at the Medium ACcess (MAC) Layer which receives packets
segregated as data and control packets, thus providing the needed decoupling of data
and control plane. We utilize the Software Defined Networking (SDN) principle of
centralized processing with segregated data and control plane to further extend the
usability of our algorithms. This dissertation introduces novel techniques in Dynamic
Bandwidth allocation, control message scheduling policy, flow control techniques and
Grouping techniques to provide improved performance in Hybrid Passive Optical Networks (PON) such as PON-xDSL, FiWi etc. Finally, we study the different types of
software defined algorithms in access networks and describe the various open challenges and research directions.
the core network thus forming the most important segment for connectivity. Access
Networks have multiple physical layer medium ranging from fiber cables, to DSL links
and Wireless nodes, creating practically-used hybrid access networks. We explore the
hybrid access network at the Medium ACcess (MAC) Layer which receives packets
segregated as data and control packets, thus providing the needed decoupling of data
and control plane. We utilize the Software Defined Networking (SDN) principle of
centralized processing with segregated data and control plane to further extend the
usability of our algorithms. This dissertation introduces novel techniques in Dynamic
Bandwidth allocation, control message scheduling policy, flow control techniques and
Grouping techniques to provide improved performance in Hybrid Passive Optical Networks (PON) such as PON-xDSL, FiWi etc. Finally, we study the different types of
software defined algorithms in access networks and describe the various open challenges and research directions.
ContributorsMercian, Anu (Author) / Reisslein, Martin (Thesis advisor) / McGarry, Michael P (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015