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- Creators: Zhang, Junshan
Description
Distributed inference has applications in fields as varied as source localization, evaluation of network quality, and remote monitoring of wildlife habitats. In this dissertation, distributed inference algorithms over multiple-access channels are considered. The performance of these algorithms and the effects of wireless communication channels on the performance are studied. In a first class of problems, distributed inference over fading Gaussian multiple-access channels with amplify-and-forward is considered. Sensors observe a phenomenon and transmit their observations using the amplify-and-forward scheme to a fusion center (FC). Distributed estimation is considered with a single antenna at the FC, where the performance is evaluated using the asymptotic variance of the estimator. The loss in performance due to varying assumptions on the limited amounts of channel information at the sensors is quantified. With multiple antennas at the FC, a distributed detection problem is also considered, where the error exponent is used to evaluate performance. It is shown that for zero-mean channels between the sensors and the FC when there is no channel information at the sensors, arbitrarily large gains in the error exponent can be obtained with sufficient increase in the number of antennas at the FC. In stark contrast, when there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the FC is shown to be no more than a factor of 8/π for Rayleigh fading channels between the sensors and the FC, independent of the number of antennas at the FC, or correlation among noise samples across sensors. In a second class of problems, sensor observations are transmitted to the FC using constant-modulus phase modulation over Gaussian multiple-access-channels. The phase modulation scheme allows for constant transmit power and estimation of moments other than the mean with a single transmission from the sensors. Estimators are developed for the mean, variance and signal-to-noise ratio (SNR) of the sensor observations. The performance of these estimators is studied for different distributions of the observations. It is proved that the estimator of the mean is asymptotically efficient if and only if the distribution of the sensor observations is Gaussian.
ContributorsBanavar, Mahesh Krishna (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Duman, Tolga (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2010
Description
This dissertation builds a clear understanding of the role of information in wireless networks, and devises adaptive strategies to optimize the overall performance. The meaning of information ranges from channel
etwork states to the structure of the signal itself. Under the common thread of characterizing the role of information, this dissertation investigates opportunistic scheduling, relaying and multicast in wireless networks. To assess the role of channel state information, the problem of opportunistic distributed opportunistic scheduling (DOS) with incomplete information is considered for ad-hoc networks in which many links contend for the same channel using random access. The objective is to maximize the system throughput. In practice, link state information is noisy, and may result in throughput degradation. Therefore, refining the state information by additional probing can improve the throughput, but at the cost of further probing. Capitalizing on optimal stopping theory, the optimal scheduling policy is shown to be threshold-based and is characterized by either one or two thresholds, depending on network settings. To understand the benefits of side information in cooperative relaying scenarios, a basic model is explored for two-hop transmissions of two information flows which interfere with each other. While the first hop is a classical interference channel, the second hop can be treated as an interference channel with transmitter side information. Various cooperative relaying strategies are developed to enhance the achievable rate. In another context, a simple sensor network is considered, where a sensor node acts as a relay, and aids fusion center in detecting an event. Two relaying schemes are considered: analog relaying and digital relaying. Sufficient conditions are provided for the optimality of analog relaying over digital relaying in this network. To illustrate the role of information about the signal structure in joint source-channel coding, multicast of compressible signals over lossy channels is studied. The focus is on the network outage from the perspective of signal distortion across all receivers. Based on extreme value theory, the network outage is characterized in terms of key parameters. A new method using subblock network coding is devised, which prioritizes resource allocation based on the signal information structure.
etwork states to the structure of the signal itself. Under the common thread of characterizing the role of information, this dissertation investigates opportunistic scheduling, relaying and multicast in wireless networks. To assess the role of channel state information, the problem of opportunistic distributed opportunistic scheduling (DOS) with incomplete information is considered for ad-hoc networks in which many links contend for the same channel using random access. The objective is to maximize the system throughput. In practice, link state information is noisy, and may result in throughput degradation. Therefore, refining the state information by additional probing can improve the throughput, but at the cost of further probing. Capitalizing on optimal stopping theory, the optimal scheduling policy is shown to be threshold-based and is characterized by either one or two thresholds, depending on network settings. To understand the benefits of side information in cooperative relaying scenarios, a basic model is explored for two-hop transmissions of two information flows which interfere with each other. While the first hop is a classical interference channel, the second hop can be treated as an interference channel with transmitter side information. Various cooperative relaying strategies are developed to enhance the achievable rate. In another context, a simple sensor network is considered, where a sensor node acts as a relay, and aids fusion center in detecting an event. Two relaying schemes are considered: analog relaying and digital relaying. Sufficient conditions are provided for the optimality of analog relaying over digital relaying in this network. To illustrate the role of information about the signal structure in joint source-channel coding, multicast of compressible signals over lossy channels is studied. The focus is on the network outage from the perspective of signal distortion across all receivers. Based on extreme value theory, the network outage is characterized in terms of key parameters. A new method using subblock network coding is devised, which prioritizes resource allocation based on the signal information structure.
ContributorsPaataguppe Suryanarayan Bhat, Chandrashekhar Thejaswi (Author) / Zhang, Junshan (Thesis advisor) / Cochran, Douglas (Committee member) / Duman, Tolga (Committee member) / Hui, Yu (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2011
Description
Ethernet switching is provided to interconnect multiple Ethernets for the exchange of Ethernet data frames. Most Ethernet switches require data buffering and Ethernet signal regeneration at the switch which incur the problems of substantial signal processing, power consumption, and transmission delay. To solve these problems, a cross bar architecture switching system for 10GBASE-T Ethernet is proposed in this thesis. The switching system is considered as the first step of implementing a multi-stage interconnection network to achieve Terabit or Petabit switching. By routing customized headers in capsulated Ethernet frames in an out-of-band control method, the proposed switching system would transmit the original Ethernet frames with little processing, thereby makes the system appear as a simple physical medium for different hosts. The switching system is designed and performed by using CMOS technology.
ContributorsLuo, Haojun (Author) / Hui, Joseph (Thesis advisor) / Zhang, Junshan (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2010
Description
With the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions of Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the artificial intelligence (AI) frontiers to the network edge to unleash the potential of the edge big data fully. This dissertation aims to comprehensively study collaborative learning and optimization algorithms to build a foundation of edge intelligence. Under this common theme, this dissertation is broadly organized into three parts. The first part of this study focuses on model learning with limited data and limited computing capability at the network edge. A global model initialization is first obtained by running federated learning (FL) across many edge devices, based on which a semi-supervised algorithm is devised for an edge device to carry out quick adaptation, aiming to address the insufficiency of labeled data and to learn a personalized model efficiently. In the second part of this study, collaborative learning between the edge and the cloud is studied to achieve real-time edge intelligence. More specifically, a distributionally robust optimization (DRO) approach is proposed to enable the synergy between local data processing and cloud knowledge transfer. Two attractive uncertainty models are investigated corresponding to the cloud knowledge transfer: the distribution uncertainty set based on the cloud data distribution and the prior distribution of the edge model conditioned on the cloud model. Collaborative learning algorithms are developed along this line. The final part focuses on developing an offline model-based safe Inverse Reinforcement Learning (IRL) algorithm for connected Autonomous Vehicles (AVs). A reward penalty is introduced to penalize unsafe states, and a risk-measure-based approach is proposed to mitigate the model uncertainty introduced by offline training. The experimental results demonstrate the improvement of the proposed algorithm over the existing baselines in terms of cumulative rewards.
ContributorsZhang, Zhaofeng (Author) / Zhang, Junshan (Thesis advisor) / Zhang, Yanchao (Thesis advisor) / Dasarathy, Gautam (Committee member) / Fan, Deliang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Voltage Source Converters (VSCs) have been widely used in grid-connected applications with Distributed Energy Resource (DER) and Electric Vehicle (EV) applications. Replacement of traditional thyristors with Silicon/Silicon-Carbide based active switches provides full control capability to the converters and allows bidirectional power flow between the source and active loads. In this study, advanced control strategies for DER inverters and EV traction inverters will be explored.Chapter 1 gives a brief introduction to State-of-the-Art of VSC control strategies and summarizes the existing challenges in different applications. Chapter 2 presents multiple advanced control strategies of grid-connected DER inverters. Various grid support functions have been implemented in simulations and hardware experiments under both normal and abnormal operating conditions. Chapter 3 proposes an automated design and optimization process of a robust H-infinity controller to address the stability issue of grid-connected inverters caused by grid impedance variation. The principle of the controller synthesis is to select appropriate weighting functions to shape the systems closed-loop transfer function and to achieve robust stability and robust performance. An optimal controller will be selected by using a 2-Dimensional Pareto Front. Chapter 4 proposes a high-performance 4-layer communication architecture to facilitate the control of a large distribution network with high Photovoltaic (PV) penetration. Multiple strategies have been implemented to address the challenges
of coordination between communication and system control and between different communication protocols, which leads to a boost in the communication efficiency and makes the architecture highly scalable, adaptive, and robust. Chapter 5 presents the control strategies of a traditional Modular Multilevel Converter (MMC) and a novel Modular Isolated Multilevel Converter (MIMC) in grid-connected and variable speed drive applications. The proposed MIMC is able to achieve great size reduction for the submodule capacitors since the fundamental and double-line frequency voltage ripple has been cancelled. Chapter 6 shows a detailed hardware and controller design for a 48 V Belt-driven Starter Generator (BSG) inverter using automotive gate driver ICs and microcontroller. The inverter prototype has reached a power density of 333
W/inch3, up to 200 A phase current and 600 Hz output frequency.
ContributorsSi, Yunpeng (Author) / Lei, Qin (Thesis advisor) / Ayyanar, Raja (Committee member) / Vittal, Vijay (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Edge networks pose unique challenges for machine learning and network management. The primary objective of this dissertation is to study deep learning and adaptive control aspects of edge networks and to address some of the unique challenges therein. This dissertation explores four particular problems of interest at the intersection of edge intelligence, deep learning and network management. The first problem explores the learning of generative models in edge learning setting. Since the learning tasks in similar environments share model similarity, it is plausible to leverage pre-trained generative models from other edge nodes. Appealing to optimal transport theory tailored towards Wasserstein-1 generative adversarial networks, this part aims to develop a framework which systematically optimizes the generative model learning performance using local data at the edge node while exploiting the adaptive coalescence of pre-trained generative models from other nodes. In the second part, a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data, is considered. The unreliable nature of wireless connectivity, togetherwith the constraints in computing resources at edge devices, dictates that the local updates at edge devices should be carefully crafted and compressed to match the wireless communication resources available and should work in concert with the receiver. Therefore, a Stochastic Gradient Descent based bandlimited coordinate descent algorithm is designed for such settings. The third part explores the adaptive traffic engineering algorithms in a dynamic network environment. The ages of traffic measurements exhibit significant variation due to asynchronization and random communication delays between routers and controllers. Inspired by the software defined networking architecture, a controller-assisted distributed routing scheme with recursive link weight reconfigurations, accounting for the impact of measurement ages and routing instability, is devised. The final part focuses on developing a federated learning based framework for traffic reshaping of electric vehicle (EV) charging. The absence of private EV owner information and scattered EV charging data among charging stations motivates the utilization of a federated learning approach. Federated learning algorithms are devised to minimize peak EV charging demand both spatially and temporarily, while maximizing the charging station profit.
ContributorsDedeoglu, Mehmet (Author) / Zhang, Junshan (Thesis advisor) / Kosut, Oliver (Committee member) / Zhang, Yanchao (Committee member) / Fan, Deliang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Mobile devices have penetrated into every aspect of modern world. For one thing, they are becoming ubiquitous in daily life. For the other thing, they are storing more and more data, including sensitive data. Therefore, security and privacy of mobile devices are indispensable. This dissertation consists of five parts: two authentication schemes, two attacks, and one countermeasure related to security and privacy of mobile devices.
Specifically, in Chapter 1, I give an overview the challenges and existing solutions in these areas. In Chapter 2, a novel authentication scheme is presented, which is based on a user’s tapping or sliding on the touchscreen of a mobile device. In Chapter 3, I focus on mobile app fingerprinting and propose a method based on analyzing the power profiles of targeted mobile devices. In Chapter 4, I mainly explore a novel liveness detection method for face authentication on mobile devices. In Chapter 5, I investigate a novel keystroke inference attack on mobile devices based on user eye movements. In Chapter 6, a novel authentication scheme is proposed, based on detecting a user’s finger gesture through acoustic sensing. In Chapter 7, I discuss the future work.
Specifically, in Chapter 1, I give an overview the challenges and existing solutions in these areas. In Chapter 2, a novel authentication scheme is presented, which is based on a user’s tapping or sliding on the touchscreen of a mobile device. In Chapter 3, I focus on mobile app fingerprinting and propose a method based on analyzing the power profiles of targeted mobile devices. In Chapter 4, I mainly explore a novel liveness detection method for face authentication on mobile devices. In Chapter 5, I investigate a novel keystroke inference attack on mobile devices based on user eye movements. In Chapter 6, a novel authentication scheme is proposed, based on detecting a user’s finger gesture through acoustic sensing. In Chapter 7, I discuss the future work.
ContributorsChen, Yimin (Author) / Zhang, Yanchao (Thesis advisor) / Zhang, Junshan (Committee member) / Reisslein, Martin (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2018
Description
The reliable operation of critical infrastructure systems is of significant importance to society. The power grid and the water distribution system are two critical infrastructure systems, each of which is facilitated by a cyber-based supervisory control and data acquisition (SCADA) system. Although critical infrastructure systems are interdependent with each other due to coupling (a power grid may be the electrical supply for a water distribution system), the corresponding SCADA systems operated independently and did not share information with each other. Modern critical infrastructure systems tend to cover a larger geographic area, indicating that a SCADA control station supervising a small area is far from meeting the demands.
In this thesis, the above-mentioned problem is addressed by building a middleware to facilitate reliable and flexible communications between two or more SCADA systems. Software Defined Networking (SDN), an emerging technology providing programmable networking, is introduced to assist the middleware. In traditional networks, network configurations required highly skilled personnel for configuring many network elements. However, SDN separates the control plane from the data plane, making network intelligence logically centralized, and leaving the forwarding switches with easy commands to follow. In this way, the underlying network infrastructures can be easily manipulated by programming, supporting the future dynamic network functions.
In this work, an SDN-assisted middleware is designed and implemented with open source platforms Open Network Operating System (ONOS) and Mininet, connecting the power grids emulator and water delivery and treatment system (WDTS) emulator EPANet. Since the focus of this work is on facilitating communications between dedicated networks, data transmissions in backbone networks are emulated. For the interfaces, a multithreaded communication module is developed. It not only enables real-time information exchange between two SCADA control centers but also supports multiple-to-multiple communications simultaneously. Human intervention is allowed in case of emergency.
SDN has many attractive benefits, however, there are still obstacles like high upgrade costs when implementing this technique. Therefore, rather than replace all the routers at once, incremental deployment of hybrid SDN networks consisting of both legacy routers and programmable SDN switches is adopted in this work. We emulate on the ratio of SDN deployment against the performance of the middleware and the results on the real dataset show that a higher fraction of SDN results in a higher reliability and flexibility of data transmissions. The middleware developed may contribute to the development of the next-generation SCADA systems.
In this thesis, the above-mentioned problem is addressed by building a middleware to facilitate reliable and flexible communications between two or more SCADA systems. Software Defined Networking (SDN), an emerging technology providing programmable networking, is introduced to assist the middleware. In traditional networks, network configurations required highly skilled personnel for configuring many network elements. However, SDN separates the control plane from the data plane, making network intelligence logically centralized, and leaving the forwarding switches with easy commands to follow. In this way, the underlying network infrastructures can be easily manipulated by programming, supporting the future dynamic network functions.
In this work, an SDN-assisted middleware is designed and implemented with open source platforms Open Network Operating System (ONOS) and Mininet, connecting the power grids emulator and water delivery and treatment system (WDTS) emulator EPANet. Since the focus of this work is on facilitating communications between dedicated networks, data transmissions in backbone networks are emulated. For the interfaces, a multithreaded communication module is developed. It not only enables real-time information exchange between two SCADA control centers but also supports multiple-to-multiple communications simultaneously. Human intervention is allowed in case of emergency.
SDN has many attractive benefits, however, there are still obstacles like high upgrade costs when implementing this technique. Therefore, rather than replace all the routers at once, incremental deployment of hybrid SDN networks consisting of both legacy routers and programmable SDN switches is adopted in this work. We emulate on the ratio of SDN deployment against the performance of the middleware and the results on the real dataset show that a higher fraction of SDN results in a higher reliability and flexibility of data transmissions. The middleware developed may contribute to the development of the next-generation SCADA systems.
ContributorsLiu, Beibei (Author) / Zhang, Junshan (Thesis advisor) / Kwan, Sau (Committee member) / Vittal, Vijay (Committee member) / Arizona State University (Publisher)
Created2018
Description
As the world embraces a sustainable energy future, alternative energy resources, such as wind power, are increasingly being seen as an integral part of the future electric energy grid. Ultimately, integrating such a dynamic and variable mix of generation requires a better understanding of renewable generation output, in addition to power grid systems that improve power system operational performance in the presence of anticipated events such as wind power ramps. Because of the stochastic, uncontrollable nature of renewable resources, a thorough and accurate characterization of wind activity is necessary to maintain grid stability and reliability. Wind power ramps from an existing wind farm are studied to characterize persistence forecasting errors using extreme value analysis techniques. In addition, a novel metric that quantifies the amount of non-stationarity in time series wind power data was proposed and used in a real-time algorithm to provide a rigorous method that adaptively determines training data for forecasts. Lastly, large swings in generation or load can cause system frequency and tie-line flows to deviate from nominal, so an anticipatory MPC-based secondary control scheme was designed and integrated into an automatic generation control loop to improve the ability of an interconnection to respond to anticipated large events and fluctuations in the power system.
ContributorsGanger, David (Author) / Vittal, Vijay (Thesis advisor) / Zhang, Junshan (Thesis advisor) / Hedman, Kory (Committee member) / Undrill, John (Committee member) / Arizona State University (Publisher)
Created2016
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