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- Genre: Doctoral Dissertation
Description
The cyber-physical systems (CPS) are emerging as the underpinning technology for major industries in the 21-th century. This dissertation is focused on two fundamental issues in cyber-physical systems: network interdependence and information dynamics. It consists of the following two main thrusts. The first thrust is targeted at understanding the impact of network interdependence. It is shown that a cyber-physical system built upon multiple interdependent networks are more vulnerable to attacks since node failures in one network may result in failures in the other network, causing a cascade of failures that would potentially lead to the collapse of the entire infrastructure. There is thus a need to develop a new network science for modeling and quantifying cascading failures in multiple interdependent networks, and to develop network management algorithms that improve network robustness and ensure overall network reliability against cascading failures. To enhance the system robustness, a "regular" allocation strategy is proposed that yields better resistance against cascading failures compared to all possible existing strategies. Furthermore, in view of the load redistribution feature in many physical infrastructure networks, e.g., power grids, a CPS model is developed where the threshold model and the giant connected component model are used to capture the node failures in the physical infrastructure network and the cyber network, respectively. The second thrust is centered around the information dynamics in the CPS. One speculation is that the interconnections over multiple networks can facilitate information diffusion since information propagation in one network can trigger further spread in the other network. With this insight, a theoretical framework is developed to analyze information epidemic across multiple interconnecting networks. It is shown that the conjoining among networks can dramatically speed up message diffusion. Along a different avenue, many cyber-physical systems rely on wireless networks which offer platforms for information exchanges. To optimize the QoS of wireless networks, there is a need to develop a high-throughput and low-complexity scheduling algorithm to control link dynamics. To that end, distributed link scheduling algorithms are explored for multi-hop MIMO networks and two CSMA algorithms under the continuous-time model and the discrete-time model are devised, respectively.
ContributorsQian, Dajun (Author) / Zhang, Junshan (Thesis advisor) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Cochran, Douglas (Committee member) / Arizona State University (Publisher)
Created2012
Description
The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.
ContributorsZhang, Rui (Author) / Zhang, Yanchao (Thesis advisor) / Duman, Tolga Mete (Committee member) / Xue, Guoliang (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid advancement of wireless technology has instigated the broad deployment of wireless networks. Different types of networks have been developed, including wireless sensor networks, mobile ad hoc networks, wireless local area networks, and cellular networks. These networks have different structures and applications, and require different control algorithms. The focus of this thesis is to design scheduling and power control algorithms in wireless networks, and analyze their performances. In this thesis, we first study the multicast capacity of wireless ad hoc networks. Gupta and Kumar studied the scaling law of the unicast capacity of wireless ad hoc networks. They derived the order of the unicast throughput, as the number of nodes in the network goes to infinity. In our work, we characterize the scaling of the multicast capacity of large-scale MANETs under a delay constraint D. We first derive an upper bound on the multicast throughput, and then propose a lower bound on the multicast capacity by proposing a joint coding-scheduling algorithm that achieves a throughput within logarithmic factor of the upper bound. We then study the power control problem in ad-hoc wireless networks. We propose a distributed power control algorithm based on the Gibbs sampler, and prove that the algorithm is throughput optimal. Finally, we consider the scheduling algorithm in collocated wireless networks with flow-level dynamics. Specifically, we study the delay performance of workload-based scheduling algorithm with SRPT as a tie-breaking rule. We demonstrate the superior flow-level delay performance of the proposed algorithm using simulations.
ContributorsZhou, Shan (Author) / Ying, Lei (Thesis advisor) / Zhang, Yanchao (Committee member) / Zhang, Junshan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2013
Description
Data centers connect a larger number of servers requiring IO and switches with low power and delay. Virtualization of IO and network is crucial for these servers, which run virtual processes for computing, storage, and apps. We propose using the PCI Express (PCIe) protocol and a new PCIe switch fabric for IO and switch virtualization. The switch fabric has little data buffering, allowing up to 512 physical 10 Gb/s PCIe2.0 lanes to be connected via a switch fabric. The switch is scalable with adapters running multiple adaptation protocols, such as Ethernet over PCIe, PCIe over Internet, or FibreChannel over Ethernet. Such adaptation protocols allow integration of IO often required for disjoint datacenter applications such as storage and networking. The novel switch fabric based on space-time carrier sensing facilitates high bandwidth, low power, and low delay multi-protocol switching. To achieve Terabit switching, both time (high transmission speed) and space (multi-stage interconnection network) technologies are required. In this paper, we present the design of an up to 256 lanes Clos-network of multistage crossbar switch fabric for PCIe system. The switch core consists of 48 16x16 crossbar sub-switches. We also propose a new output contention resolution algorithm utilizing an out-of-band protocol of Request-To-Send (RTS), Clear-To-Send (CTS) before sending PCIe packets through the switch fabric. Preliminary power and delay estimates are provided.
ContributorsLuo, Haojun (Author) / Hui, Joseph (Thesis advisor) / Song, Hongjiang (Committee member) / Reisslein, Martin (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2013
Description
A unique feature, yet a challenge, in cognitive radio (CR) networks is the user hierarchy: secondary users (SU) wishing for data transmission must defer in the presence of active primary users (PUs), whose priority to channel access is strictly higher.Under a common thread of characterizing and improving Quality of Service (QoS) for the SUs, this dissertation is progressively organized under two main thrusts: the first thrust focuses on SU's throughput by exploiting the underlying properties of the PU spectrum to perform effective scheduling algorithms; and the second thrust aims at another important QoS performance of the SUs, namely delay, subject to the impact of PUs' activities, and proposes enhancement and control mechanisms. More specifically, in the first thrust, opportunistic spectrum scheduling for SU is first considered by jointly exploiting the memory in PU's occupancy and channel fading. In particular, the underexplored scenario where PU occupancy presents a {long} temporal memory is taken into consideration. By casting the problem as a partially observable Markov decision process, a set of {multi-tier} tradeoffs are quantified and illustrated. Next, a spectrum shaping framework is proposed by leveraging network coding as a {spectrum shaper} on the PU's traffic. Such shaping effect brings in predictability of the primary spectrum, which is utilized by the SUs to carry out adaptive channel sensing by prioritizing channel access order, and hence significantly improve their throughput. On the other hand, such predictability can make wireless channels more susceptible to jamming attacks. As a result, caution must be taken in designing wireless systems to balance the throughput and the jamming-resistant capability. The second thrust turns attention to an equally important performance metric, i.e., delay performance. Specifically, queueing delay analysis is conducted for SUs employing random access over the PU channels. Fluid approximation is taken and Poisson driven stochastic differential equations are applied to characterize the moments of the SUs' steady-state queueing delay. Then, dynamic packet generation control mechanisms are developed to meet the given delay requirements for SUs.
ContributorsWang, Shanshan (Author) / Zhang, Junshan (Thesis advisor) / Xue, Guoliang (Committee member) / Hui, Joseph (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Air pollution is one of the biggest challenges people face today. It is closely related to people's health condition. The agencies set up standards to regulate the air pollution. However, many of the pollutants under the regulation level may still result in adverse health effect. On the other hand, it is not clear the exact mechanism of air pollutants and its health effect. So it is difficult for the health centers to advise people how to prevent the air pollutant related diseases. It is of vital importance for both the agencies and the health centers to have a better understanding of the air pollution. Based on these needs, it is crucial to establish mobile health sensors for personal exposure assessment. Here, two sensing principles are illustrated: the tuning fork platform and the colorimetric platform. Mobile devices based on these principles have been built. The detections of ozone, NOX, carbon monoxide and formaldehyde have been shown. An integrated device of nitrogen dioxide and carbon monoxide is introduced. Fan is used for sample delivery instead pump and valves to reduce the size, cost and power consumption. Finally, the future work is discussed.
ContributorsWang, Rui (Author) / Tao, Nongjian (Thesis advisor) / Forzani, Erica (Committee member) / Zhang, Yanchao (Committee member) / Karam, Lina (Committee member) / Arizona State University (Publisher)
Created2012
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
Recent advances in cyber-physical systems, artificial intelligence, and cloud computing have driven the widespread deployment of Internet-of-Things (IoT) devices in smart homes. However, the spate of cyber attacks exploiting the vulnerabilities and weak security management of smart home IoT devices have highlighted the urgency and challenges of designing efficient mechanisms for detecting, analyzing, and mitigating security threats towards them. In this dissertation, I seek to address the security and privacy issues of smart home IoT devices from the perspectives of traffic measurement, pattern recognition, and security applications. I first propose an efficient multidimensional smart home network traffic measurement framework, which enables me to deeply understand the smart home IoT ecosystem and detect various vulnerabilities and flaws. I further design intelligent schemes to efficiently extract security-related IoT device event and user activity patterns from the encrypted smart home network traffic. Based on the knowledge of how smart home operates, different systems for securing smart home networks are proposed and implemented, including abnormal network traffic detection across multiple IoT networking protocol layers, smart home safety monitoring with extracted spatial information about IoT device events, and system-level IoT vulnerability analysis and network hardening.
ContributorsWan, Yinxin (Author) / Xue, Guoliang (Thesis advisor) / Xu, Kuai (Thesis advisor) / Yang, Yezhou (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2023
Description
Software Defined Networking has been the primary component for Quality of Service provisioning in the last decade. The key idea in such networks is producing independence between the control and the data-plane. The control plane essentially provides decision making logic to the data-plane, which in-turn is only responsible for moving the packets from source to destination based on the flow-table entries and actions. In this thesis an in-depth design and analysis of Software Defined Networking control plane architecture for Next Generation Networks is provided. Typically, Next Generation Networks are those that need to satisfy Quality of Service restrictions (like time bounds, priority, hops, to name a few) before the packets are in transit. For instance, applications that are dependent on prediction popularly known as ML/AI applications have heavy resource requirements and require completion of tasks within the time bounds otherwise the scheduling is rendered useless. The bottleneck could be essentially on any layer of the network stack, however in this thesis the focus is on layer-2 and layer-3 scheduling. To that end, the design of an intelligent control plane is proposed by paying attention to the scheduling, routing and admission strategies which are necessary to facilitate the aforementioned applications requirement. Simulation evaluation and comparisons with state of the art approaches is provided withreasons corroborating the design choices. Finally, quantitative metrics are defined and measured to justify the benefits of the designs.
ContributorsBalasubramanian, Venkatraman (Author) / Reisslein, Martin (Thesis advisor) / Suppappola, Antonia Papandreou (Committee member) / Zhang, Yanchao (Committee member) / Thyagaturu, Akhilesh (Committee member) / Arizona State University (Publisher)
Created2022
Description
Individuals and organizations have greater access to the world's population than ever before. The effects of Social Media Influence have already impacted the behaviour and actions of the world's population. This research employed mixed methods to investigate the mechanisms to further the understand of how Social Media Influence Campaigns (SMIC) impact the global community as well as develop tools and frameworks to conduct analysis. The research has qualitatively examined the perceptions of Social Media, specifically how leadership believe it will change and it's role within future conflict. This research has developed and tested semantic ontological modelling to provide insights into the nature of network related behaviour of SMICs. This research also developed exemplar data sets of SMICs. The insights gained from initial research were used to train Machine Learning classifiers to identify thematically related campaigns. This work has been conducted in close collaboration with Alliance Plus Network partner, University of New South Wales and the Australian Defence Force.
ContributorsJohnson, Nathan (Author) / Reisslein, Martin (Thesis advisor) / Turnbull, Benjamin (Committee member) / Zhao, Ming (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2022