Browsing by Author "Han, Zhu"
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Item A Cooperative Bayesian Nonparametric Framework for Primary User Activity Monitoring in Cognitive Radio Networks(IEEE Journal on Selected Areas in Communications, 2/2/2012) Saad, Walid; Han, Zhu; Poor, H. Vincent; Ba?ar, Tamer; Song, Jin BinThis paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users (PUs), to cooperate and use Bayesian nonparametric techniques to estimate the distributions of the PUs' activity pattern. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use Bayesian nonparametric techniques so as to improve the accuracy of the estimated PUs' activity distributions. Simulation results show that the proposed algorithm significantly improves the estimates of the PUs' activity patterns.Item A FRAMEWORK OF BELIEF PROPAGATION AND GAME THEORY FOR COGNITIVE RADIO SECURITY AND ROUTING(2012-08) Yuan, Zhou; Han, Zhu; Ogmen, Haluk; Shih, Wei-Chuan; Zheng, Rong; Qian, LijunWith the advent of new high data rate wireless applications, as well as growth of existing wireless services, demand for additional bandwidth is increasing rapidly. Existing spectrum allo-cation policies of the Federal Communications Commission (FCC) prohibits unlicensed access to licensed spectrum, constraining them instead to several heavily populated, interference-prone fre-quency bands, which causes spectrum scarcity. However, it has been shown by several spectrum measurement campaigns that the current licensed spectrum usage across time and frequency is in-efficient. Therefore, a concept of unlicensed users temporarily “borrowing” spectrum from incum-bent license holders to improve the spectrum utilization, called dynamic spectrum access (DSA), is proposed. Cognitive radio is a communication paradigm that employs software-defined radio tech-nology in order to perform DSA and others versatile, powerful and portable wireless transceivers. Up until now, most existing works have focused on spectrum sensing and spectrum access, but very few have focused on the higher layer, which is very important for cognitive radio networks. In this dissertation, we use the framework of distributed game theory and belief propagation to explore the routing techniques and the security issues in cognitive radio networks. Firstly, a belief-propagation based defense strategy for the primary user emulation (PUE) attack in cognitive radio networks is proposed, which avoids the deployment of additional sensor networks and expensive hardware in the networks used in the existing literatures. The proposed algorithm can provide low computational complexity and fast execution speed, and the framework is flexible to incorporate to defeat various kinds of attacks for future extension. In the next section, a brand new network-layer attack, named routing toward primary user (RPU) attack, is discovered in cognitive radio networks, in which malicious secondary users will try to route the data to those secondary users which are closer to the primary users in order to increase interference to the primary users. This new attack is very difficult to detect because the malicious nodes may claim that those nodes, to which they forward the packets, behave dishonestly and cause problems in the data transmission. Also a belief-propagation based defense algorithm is proposed in which each node keeps a table recording the feedbacks from the other nodes on the route, exchanges feedback information, computes beliefs and detects the malicious nodes based on the final belief values. Simulation results show that the proposed defense strategy against the RPU attack is effective and efficient in terms of significant reduction in the delay and interference caused by the RPU attack. Finally, we propose a distributed routing algorithm using the network formation game to minimize the aggregate interference from the secondary users to the primary users while keeping the delay along the route low. The proposed distributed routing algorithm can avoid the problems in the centralized routing solution, such as the high cost for building the centralized coordinate nodes, high information-gathering delay, and system breakdown caused by the possible failures in the centralized nodes, and is practically implementable. Simulation results show that the proposed scheme finds better routes in terms of interference to the primary users compared to the shortest path scheme, and the distributed solution shows near optimum compared to the centralized solution. The proposed technologies concern-ing the security issues and the routing algorithms in cognitive radio networks can provide a lot of benefits to society, and will assist the public safety, emergency services, and first responders communities in enabling better communications access to the network, which could potentially translate into additional human lives being saved.Item A Game-Theoretic Approach to Energy Trading in the Smart Grid(IEEE Transactions on Smart Grid, 4/15/2014) Wang, Yunpeng; Saad, Walid; Han, Zhu; Poor, H. Vincent; Ba?ar, TamerElectric storage units constitute a key element in the emerging smart grid system. In this paper, the interactions and energy trading decisions of a number of geographically distributed storage units are studied using a novel framework based on game theory. In particular, a noncooperative game is formulated between storage units, such as plug-in hybrid electric vehicles, or an array of batteries that are trading their stored energy. Here, each storage unit's owner can decide on the maximum amount of energy to sell in a local market so as to maximize a utility that reflects the tradeoff between the revenues from energy trading and the accompanying costs. Then in this energy exchange market between the storage units and the smart grid elements, the price at which energy is traded is determined via an auction mechanism. The game is shown to admit at least one Nash equilibrium and a novel algorithm that is guaranteed to reach such an equilibrium point is proposed. Simulation results show that the proposed approach yields significant performance improvements, in terms of the average utility per storage unit, reaching up to 130.2% compared to a conventional greedy approach.Item A Hierarchical Game Framework for Distributive Resource Allocation in Future Heterogeneous Network(2017-12) Zhang, Huaqing; Han, Zhu; Pan, Miao; Nguyen, Hien Van; Cai, Lin X.; Niyato, DusitThe explosive development of mobile data service makes our lives convenient and efficient. However, with the increasing demands of wireless data transmission, it is difficult to fulfill real-time requirements of mobile users in the traditional cellular architecture. In future wireless communication, the network is expected to be heterogeneous. On one hand, the large amount and different sizes of small cells are expected to be overlaid within the wireless network. On the other hand, wireless networks will be coordinated with other networks for expansion of available resources. Nevertheless, due to the distributive behaviors of multiple individuals in the heterogeneous network (HetNet), it is challenging to adopt resource allocations to achieve stable and high quality of service (QoS) for all mobile users. In this dissertation, we overview the development of wireless networks and summarize the wireless service into a 4-layer service architecture, consisting of the service layer, resource layer, infrastructure layer and user layer. Considering the heterogeneous architecture of future wireless network, a hierarchical game framework is proposed to determine distributive strategies for high performance and equilibrium solutions. We first analyze the distributive behaviors during the cooperation of multiple infrastructure providers, and propose a zero-determinant strategy for the administrator of the cooperation to maintain a high social welfare. Then, we analyze the distributive behaviors of multiple resource providers as well as infrastructure providers, with the applications of LTE unlicensed (LTE-U) and visible light communication (VLC). In LTE-U, multi-leader multi-follower Stackelberg game is employed among operators and users for resource management of licensed spectrum and unlicensed spectrum. In VLC, we combine VLC with Device-to-Device (D2D) communication and employ the Stackelberg game with a graphical game to analyze the equilibrium behaviors of all individuals. Finally, we consider the general heterogeneous network with the application of fog computing. With network virtualization, a hierarchical game framework combining the Stackelberg game and matching game are applied, where each mobile user is allocated with the optimal amount of computing resources from the selected fog node or cloud server.Item A Hierarchical Game With Strategy Evolution for Mobile Sponsored Content and Service Markets(IEEE Transactions on Communications, 9/24/2018) Wang, Wenbo; Xiong, Zehui; Niyato, Dusit; Wang, Ping; Han, ZhuIn sponsored content and service markets, the content and service providers are able to subsidize their target mobile users through directly paying the mobile network operator to lower the price of the data/service access charged by the network operator to the mobile users. The sponsoring mechanism leads to a surge in mobile data and service demand, which in return compensates for the sponsoring cost and benefits the content/service providers. In this paper, we study the interactions among the three parties in the market, namely, the mobile users, the content/service providers, and the network operator, as a two-level game with multiple Stackelberg (i.e., leader) players. Our study is featured by the consideration of global network effects owning to consumers' grouping. Since the mobile users may have bounded rationality, we model the service-selection process among them as an evolutionary-population follower sub-game. Meanwhile, we model the pricing-then-sponsoring process between the content/service providers and the network operator as a non-cooperative equilibrium searching problem. By investigating the structure of the proposed game, we reveal a few important properties regarding the equilibrium existence and propose a distributed, projection-based algorithm for iterative equilibrium searching. Simulation results validate the convergence of the proposed algorithm and demonstrate how sponsoring helps improve both the providers' profits and the users' experience.Item A Medium Voltage Cascaded Multi-level Converter with Isolated High Frequency Link Using SiC Switching Devices(2016-08) Esho, Alaba Olumide; Abolhassani, Mehdi T.; Shireen, Wajiha; Han, ZhuIn this thesis, a new medium voltage multi-level power converter using low voltage SiC devices that would result in an overall efficiency improvement in harnessing renewable energies was analyzed. The topology is based on cascading low voltage power stages that utilize isolated DC-DC converter. The units to be improved are the DC-DC converter, which was used in conjunction with a rectifier (depending on the nature of the renewable energy source- AC or DC power source) and an inverter at the output stage. The DC-DC converter is a Dual Active Bridge with a high frequency link; the high frequency link is possible based on the high-frequency transformer (HFT) and wide band gap switch (silicon carbide (SiC) MOSFET) that was adopted in this topology. Due to the inverse relationship between frequency and the size of magnetic components, the power density of the converter is high, which in-turn gives a higher efficiency. Apart from these benefits, there are a number of advantages in this topology such as the isolated link, the multilevel output, the variable frequency output, the compatibility with High Voltage Direct Current (HVDC) etc. In this case, the generation units could be offshore or in a remote on-shore area due to environmental or aesthetic reasons. One of the major factors that discourage harnessing of renewable energy is the initial cost involved; the topology chosen naturally minimizes the number of components involved and this, in turn, reduces the cost and the failure rate. The final output is a high power quality multilevel AC voltage with low concerns for electromagnetic compatibility. The Simulations were carried out using a library in MATLAB (Simelectronics/Simscape).Item A NONPARAMETRIC BAYESIAN FRAMEWORK FOR MOBILE DEVICE SECURITY AND LOCATION BASED SERVICES(2013-12) Nguyen, Nam T. 1979-; Han, Zhu; Zheng, Rong; Ogmen, Haluk; Prasad, Saurabh; Pan, MiaoIn June 2013, it was reported that, for the first time, more than half of American adults have smartphones [1]. Smartphones are carried by the users most of the time and used to access all types of personal sensitive information, from email, Facebook to banking, files server, etc. The fact that humans are more and more attached to their phones poses both good and bad aspects, namely, security challenges and new opportunities in improving users experiences. From the bad aspect, it is of interest to find out how to protect the users from cyber attack. Whereas from the good aspect, we can improve users’ experiences by providing services related to their locations. To address the first problem, in this dissertation, we propose a security framework to detect two kinds of attacks, the Masquerade attack and the Sybil attack. Most existing literature employs supervised learning and assumes the number of devices is known. We, on the other hand, propose a non-parametric Bayesian method to detect the number of devices as well as determine which observations belong to which devices in an unsupervised passive manner. An attack can be detected by comparing the number of registered users with the number of devices found, and the malicious nodes are found based on the labels of their observations. For the second problem, we propose a location based service (LBS) enabler framework by providing a high accuracy indoor location identification and future location prediction algorithms. LBS are applications in which, locations of users are utilized to activate a set of services which significantly improve users experiences. Examples include a micro-climate control application, which can automatically adjust room temperature given that the room is occupied. It also can be a network scheduling users’ access application, where users’ future whereabouts can be predicted and used for arranging files transfer to better enhance users’ experiences. In this dissertation, we mainly focus our research on the above two fields. The nonparametric Bayesian framework was used as the generative model for both the observations extracted from the wireless signal in the wireless security problem, as well the observations extracted from the features that represent a location in LBS. Beside the framework, the major contributions of the dissertation include a missing data handling algorithm, a light-weight indoor place identification algorithm, a stopping rule to terminate the algorithm in a quickest way while maintaining a acceptable false alarm rate, and a passive approach to defend against Masquerade and Sybil attacks in wireless networks. Moreover, several mechanisms to predict users’ future whereabouts such as a Dynamic Hidden Markov Model that can evolve itself over time, or a prediction model based on Deep Learning were proposed. Most of the algorithms are evaluated using experimental data and proved to obtain considerably high performances compared with other state-of-the-art approaches.Item A Quickest Detection Framework for Smart Grid(2013-05) Huang, Yi 1984-; Han, Zhu; Ogmen, Haluk; Khodaei, Amin; Zheng, Rong; Qian, LijunThe smart grid technology has significantly enhanced the robustness and efficiency of the traditional power grid network. The integration of such smart functionalities into the power grid also poses many risks such as increasing system complexity, network security risk, end-user data privacy issues, uncertainty of the renewable energy generation, and etc. Although the smart grid has been investigated heavily in many directions and aspects when it was raised for the first time, the research on the power system issues and the quickest detection techniques on smart grid networks are still limited. In this dissertation, we explore specifically in three areas: system status, security issue, and resource management in smart grid networks. First, we propose a CUSUM-based defense strategy against the false data injection attack in smart grid networks. In comparison to classical approaches, the advantages of the proposed CUSUM-based defense mechanism include the low complexity approach of solving unknown parameters in the probability density function of post change distribution, and the development of Markov chain based model for analyzing the proposed approach for performance guarantee. Second, we propose a quickest estimation scheme to determine the network topology with minimum detection/decision delay while maintaining a given accuracy constraints from the dispersive environment. The conventional topology estimation requires a long process of network status analysis for ensuring the normality. The proposed algorithm helps detect and identify the topological error efficiently and promptly for smart grid state estimation via just using online power measurement, and furthermore, reduce on vulnerability on system failure. Finally, we investigate the energy profile allocation scheme for end-user that is capable of determining the best choice of energy profiles as few samples as possible for long-term usage under the accuracy constraint while balancing the exploration and exploitation. In other words, an online learning technique is developed to learn the evolution of the power pattern in terms of reliability over time. We derive the close form for the confident interval and obtain an upper bound for the expected regret for the proposed scheme. In conclusion, the proposed technologies concerning different aspects of smart grid issues, such as cyber security issues, network topology problem, alternative renewable energy resource allocation, can provide a lot of benefits to a power grid society, and will enhance the grid reliability and stability, utility services, emission control, and end-user experience in enabling better communications access to the grid, which could potentially translate into effective efficient utility operations and better living environment for human beings.Item A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks(IEEE Communications Surveys & Tutorials, 3/9/2016) Wang, Wenbo; Kwasinski, Andres; Niyato, Dusit; Han, ZhuThe framework of cognitive wireless networks is expected to endow the wireless devices with the cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In many practical scenarios, the complexity of network dynamics makes it difficult to determine the network evolution model in advance. Thus, the wireless decision-making entities may face a black-box network control problem and the model-based network management mechanisms will be no longer applicable. In contrast, model-free learning enables the decision-making entities to adapt their behaviors based on the reinforcement from their interaction with the environment and (implicitly) build their understanding of the system from scratch through trial-and-error. Such characteristics are highly in accordance with the requirement of cognition-based intelligence for devices in cognitive wireless networks. Therefore, model-free learning has been considered as one key implementation approach to adaptive, self-organized network control in cognitive wireless networks. In this paper, we provide a comprehensive survey on the applications of the state-of-the-art model-free learning mechanisms in cognitive wireless networks. According to the system models on which those applications are based, a systematic overview of the learning algorithms in the domains of single-agent system, multiagent systems, and multiplayer games is provided. The applications of model-free learning to various problems in cognitive wireless networks are discussed with the focus on how the learning mechanisms help to provide the solutions to these problems and improve the network performance over the model-based, non-adaptive methods. Finally, a broad spectrum of challenges and open issues is discussed to offer a guideline for the future research directions.Item Achievable and Crystallized Rate Regions of the Interference Channel with Interference as Noise(IEEE Transactions on Wireless Communications, 1/6/2012) Charafeddine, Mohamad Awad; Sezgin, Aydin; Han, Zhu; Paulraj, ArogyaswamiThe interference channel achievable rate region is presented when the interference is treated as noise. The formulation starts with the 2-user channel, and then extends the results to the n-user case. The rate region is found to be the convex hull of the union of n power control rate regions, where each power control rate region is upperbounded by a (n-1)-dimensional hyper-surface characterized by having one of the transmitters transmitting at full power. The convex hull operation lends itself to a time-sharing operation depending on the convexity behavior of those hyper-surfaces. In order to know when to use time-sharing rather than power control, the paper studies the hyper-surfaces convexity behavior in details for the 2-user channel with specific results pertaining to the symmetric channel. It is observed that most of the achievable rate region can be covered by using simple On/Off binary power control in conjunction with time-sharing. The binary power control creates several corner points in the n-dimensional space. The crystallized rate region, named after its resulting crystal shape, is hence presented as the time-sharing convex hull imposed onto those corner points; thereby offering a viable new perspective of looking at the achievable rate region of the interference channel.Item Advances in Raman and Surface-Enhanced Raman Spectroscopy: Instrumentation, Plasmonic Engineering and Biomolecular Sensing(2014-08) Qi, Ji; Shih, Wei-Chuan; Wolfe, John C.; Han, Zhu; Larin, Kirill V.; Willson, Richard C.; Roysam, BadrinathRaman spectroscopy is a powerful technique for label-free molecular sensing and imaging in various fields. High molecular specificity, non-invasive sampling approach and the need for little or no sample preparation make Raman spectroscopy uniquely advantageous compared to other analytical techniques. However, Raman spectroscopy suffers from the intrinsic limitation of weak signal intensity. Therefore, time-sensitive studies such as diagnosis and clinical applications require improving the throughput of Raman instrumentation. Alternatively, surface-enhanced Raman scattering (SERS) improves the sensitivity by 10^6 to 10^14 times, making the weak Raman intensity no longer a limitation. Nevertheless, it is still a big challenge to engineer plasmonic substrates with high SERS enhancement, good uniformity and reproducibility. This thesis presents advances in: (1) Raman instrumentation towards high-throughput, environmental, biological and biomedical analysis; (2) SERS substrates with high enhancement factor (EF), uniformity and reproducibility; (3) biosensing applications including imaging of cell population and detection of biomolecules towards high time efficiency and sensitivity. In Raman instrumentation, we have built a high-throughput line-scan Raman microscope system and a novel parallel Raman microscope based on multiple-point active-illumination and wide-field hyperspectral data collection. Using the line-scan Raman microscope, we have performed chemical imaging of intact biological cells at the cell population level. We have also demonstrated more flexibility and throughput from the active-illumination Raman microscope in rapid chemical identification and screening of micro and nanoparticles and bacterial spores. Both Raman microscopes have been used to evaluate the large-area SERS uniformity of DC-sputtered gold nanoislands, a low-cost means to fabricate plasmonic substrates. In plasmonic engineering, we have introduced patterned nanoporous gold nanoparticles that feature 3-dimensional mesoporous network with pore size on the order of 10 nm throughput the sub-wavelength nanoparticles. We showed that the plasmonic resonance can be tuned by geometrical engineering of either the external nanoparticle size and shape or the nanoporous network. As an example, we have developed disk-shaped entities, also known as nanoporous gold disks (NPGD) with highly uniform and reproducible SERS EF exceeding 10^8. Label-free, multiplexed molecular sensing and imaging has been demonstrated on NPGD substrates. Using the line-scan Raman microscope and the NPGD substrates, we have successfully developed a label-free DNA hybridization sensor at the single-molecule level in microfluidics. We have observed discrete, individual DNA hybridization events by in situ monitoring the hybridization process using SERS. The advances and promising results presented in this thesis demonstrate potential impact in Raman/SERS imaging and sensing in environmental, biological and biomedical applications.Item An Empirical characterization of concrete channel and modulation schemes with piezoelectric transducers based transceivers(2012-08) Kailaswar, Sai Shiva 1989-; Zheng, Rong; Han, Zhu; Chen, YuhuaStructural Health Monitoring plays a vital role in improving the safety and maintainability of critical engineering structures. A network comprised of Piezo-electric sensors and actuators has been devised to monitor the condition of concrete structures. A feasible data communication mechanism for sensor networks is needed to ensure effective transmission of information regarding the structural health conditions from sensor nodes to the central processing unit. This thesis work lays the foundation toward designing a communication system using stress waves modulated with information inside concrete structure. The following tasks are essential in designing an effective communication system, namely, i) measurements of concrete channel response, ii) measurements of different modulation schemes, and iii) the design of receiver amplifier based on Automatic gain control technique. The proposed solution utilizes GNU Radio, a software development toolkit to implement different modulation schemes and Universal Software Radio Peripheral to connect the hardware (concrete channel) with software-defined radios.Item Applications of Economic and Pricing Models for Wireless Network Security: A Survey(IEEE Communications Surveys & Tutorials, 7/27/2017) Luong, Nguyen Cong; Hoang, Dinh Thai; Wang, Ping; Niyato, Dusit; Han, ZhuThis paper provides a comprehensive literature review on applications of economic and pricing theory to security issues in wireless networks. Unlike wireline networks, the broadcast nature and the highly dynamic change of network environments pose a number of nontrivial challenges to security design in wireless networks. While the security issues have not been completely solved by traditional or system-based solutions, economic and pricing models recently were employed as one efficient solution to discourage attackers and prevent attacks to be performed. In this paper, we review economic and pricing approaches proposed to address major security issues in wireless networks including eavesdropping attack, denial-of-service (DoS) attack such as jamming and distributed DoS, and illegitimate behaviors of malicious users. Additionally, we discuss integrating economic and pricing models with cryptography methods to reduce information privacy leakage as well as to guarantee the confidentiality and integrity of information in wireless networks. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to wireless security issues.Item Applications of Repeated Games in Wireless Networks: A Survey(IEEE Communications Surveys & Tutorials, 6/16/2015) Hoang, Dinh Thai; Lu, Xiao; Niyato, Dusit; Wang, Ping; Kim, Dong In; Han, ZhuA repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model interactions among players in only one period, in repeated games, interactions of players repeat for multiple periods. Thus, the players become aware of other players' past behaviors and their future benefits, so as to adapt their strategies accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games in encouraging wireless nodes into cooperations, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Item Applied Machine Learning with Latent Space Representation and Manipulation(2020-08) Du, Xunsheng; Han, Zhu; Nguyen, Hien Van; Chen, Jiefu; Wu, Xuqing; Cheng, ShuxingMachine learning is one of the most promising fields of study nowadays. It is applied to various types of industry including image classification, object detection, and time-series signals prediction, etc. Latent space is a concept that is hidden but significant to machine learning, which helps extract features of data from different dimensions. In this dissertation, we try to apply machine learning with latent space representation and manipulation in real industrial applications both with two studies, respectively. We first apply machine learning with latent space representation to two works. First, we study the vehicle-to-vehicle relay networks with latent space to represent the decision of resource allocation in the reinforcement learning context. We propose a deep reinforcement learning model to decide the vehicles to be the relay. With the proposed model, the optimal decision is made and the largest overall data allocation is achieved. Then, we conduct a quantitative analysis of the cutting volume in real-time. This analysis is traditionally accomplished by workers on the rig, which cannot guarantee real-time and consistent reports of the cutting volume. With the proposed method, we are able to monitor the cutting volume in a real-time manner while relieving human labor. We then apply machine learning with latent space manipulation to another two works. First, we monitor the distribution of buffelgrass, a type of invasive grass based on the remote sensing images taken by unnamed aerial vehicles. By applying deep learning along with the discrete latent space-assisted data augmentation, the buffelgrass patterns are accurately located. Second, we solve a seismic inversion problem which is a workflow for deriving the subsurface model from seismic measurements. We propose to utilize autoencoder deep networks with latent space-aligned domain adaptation to migrate the trained model to unexploited data. With the proposed method, we prototype an inversion model with generalization capability quickly in a similar scenario.Item Asset Analytics of Smart Grid Infrastructure for Resiliency Enhancement(2015-05) Arab, Ali; Khator, Suresh K.; Han, Zhu; Lim, Gino J.; Tekin, Eylem; Khodaei, AminFirst, a post-hurricane restoration model for power grid which considers the economics of disaster is introduced. The physical and economic constraints of the system, including unit commitment and restoration constraints, are incorporated in the proposed model. The aim is to restore the hurricane-related damages to electric power system infrastructure in an economic and customer-centered manner, without violating the physics of the system, in order to mitigate the aftermath of natural disasters. Second, a proactive resource allocation model for repair and restoration of potential damages to the power system infrastructure located on the path of an upcoming hurricane is proposed. The objective is to develop an efficient framework for system operators to restore potential damages to power system components in a cost-effective manner. The problem is modeled as a two-stage stochastic integer program with recourse. This model can improve proactive preparedness of the decision makers to cope with emergencies, especially those of nature origins, in order to minimize the restoration cost, and enhance the resilience of the power system. Third, a model is proposed to incorporate the impact of potential damage due to hurricane in the maintenance scheduling of the power infrastructure components located in hurricane prone areas. The power infrastructure deterioration process, as well as two competing and independent failure modes, i.e., failure due to loss of reliability and failure due to hurricane damages are integrated into the model. Moreover, the interrelationship between the component, the grid, and the associated downtime cost dynamics are analyzed. The problem is modeled as a Markov decision process with perfect state information. Fourth, the impact of El Nino/La Nina phenomenon which has shown to induce seasonal effects on hurricane arrivals in long-term climatological horizon is considered in asset management strategies of the electric power systems. An integrated infrastructure hardening and condition-based maintenance scheduling model for critical components of the power systems is developed. The partially observable Markov decision processes are used to formulate the problem. The survival function against hurricane is derived as a dynamic stress-strength model, and is incorporated in the proposed framework.Item Asymptotic Analysis of Large Cooperative Relay Networks Using Random Matrix Theory(EURASIP Journal on Advances in Signal Processing, 3/5/2008) Li, Husheng; Han, Zhu; Poor, H. VincentCooperative transmission is an emerging communication technology that takes advantage of the broadcast nature of wireless channels. In cooperative transmission, the use of relays can create a virtual antenna array so that multiple-input/multiple-output (MIMO) techniques can be employed. Most existing work in this area has focused on the situation in which there are a small number of sources and relays and a destination. In this paper, cooperative relay networks with large numbers of nodes are analyzed, and in particular the asymptotic performance improvement of cooperative transmission over direction transmission and relay transmission is analyzed using random matrix theory. The key idea is to investigate the eigenvalue distributions related to channel capacity and to analyze the moments of this distribution in large wireless networks. A performance upper bound is derived, the performance in the low signal-to-noise-ratio regime is analyzed, and two approximations are obtained for high and low relay-to-destination link qualities, respectively. Finally, simulations are provided to validate the accuracy of the analytical results. The analysis in this paper provides important tools for the understanding and the design of large cooperative wireless networks.Item Attack and Defend Mechanisms for State Estimation in Smart Grid(2013-08) Esmalifalak, Mohammad; Han, Zhu; Ogmen, Haluk; Zheng, Rong; Khodaei, Amin; Xie, LeAging power industries together with an increase in the demand from industrial and residential customers are the main incentive for policy makers to define a road map to the next generation power system called the smart grid. Changing the traditional structure of power systems and integrating communication devices are beneficial for better monitoring and decision making by the system operators, but at the same time it increases the risk of cyber attacks. Power system blackout in 2003 created serious problems for customers in the eastern US and Canada. Although different investigations report reasons other than cyber attack for the blackout, many researchers believe a similar tragedy could happen with targeted cyber attacks. Later in 2007, researchers at the Idaho National Lab tried to attack a synchronous generator. The attack was successful and the generator was self-destroyed in a couple of minutes. This attack alarmed cyber-security decision makers, motivating them to define a critical infrastructure that is vulnerable to cyber-attack. An example of this vulnerability is the current bad data detection routine in state estimation, which is not able to detect a certain type of cyber attack called \emph{stealth attack}. Stealth attacks are able to manipulate the state estimation results in order to take economical advantages or make technical problems for power grid. In this dissertation, we analyze the cyber attack against state estimation, from both the attacker and defender points of views. We first review the structure of the electricity market, and then we present the way that the attacker alters the congestion in the ex--post market (in the desired direction) and makes financial profits. We investigate the case that attackers without prior knowledge of the power grid topology, try to make inferences through phasor observations. The inferred structural information is used to launch stealth attacks. This attack is formulated to change the price of electricity in the real-time market. Second, we look at the false data injection from the defender point of view. Because of a huge number of measurements in the network, attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero-sum game between the attacker and the defender, and we describe how the interest of one party (attacker or defender) can influence the other's interest. The results of this game defines the proportion of times that the attacker and defender will attack and defend different measurements, respectively. Finally, we illustrate how the normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We first propose two machine learning based techniques for stealthy attack detection. The first method utilizes the supervised learning over labeled data and trains a support vector machine. The second method requires no labeled outputs for training data and detects deviation in the measurements. In both methods, principle component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computational complexities.Item Auction-Based Resource Allocation for Cooperative Video Transmission Protocols over Wireless Networks(EURASIP Journal on Advances in Signal Processing, 6/7/2009) Han, Zhu; Su, Guan-Ming; Wang, Haohong; Ci, Song; Su, WeifengCooperative transmission has been proposed as a novel transmission strategy that takes advantage of broadcast nature of wireless networks, forms virtual MIMO system, and provides diversity gains. In this paper, wireless video transmission protocols are proposed, in which the spectrum resources are first allocated for the source side to broadcast video packets to the relay and destination, and then for the relay side to transmit side information generated from the received packets. The proposed protocols are optimized to minimize the end-to-end expected distortion via choosing bandwidth/power allocation, configuration of side information, subject to bandwidth and power constraints. For multiuser cases, most of current resource allocation approaches cannot be naturally extended and applied to the networks with relay nodes for video transmission. This paper extends the share auction approach into the cooperative video communication scenarios and provides a near-optimal solution for resource allocation. Experimental results have demonstrated that the proposed approach has significant advantage of up to 4?dB gain in single user case and 1.3?dB gain in multiuser case over the reference systems in terms of peak-to-signal-noise ratio. In addition, it reduces the formidable computational complexity of the optimal solution to linear complexity with performance degradation of less than 0.3?dB.Item ?-Augmented Tree for Robust Data Collection in Advanced Metering Infrastructure(International Journal of Distributed Sensor Networks, 3/29/2016) Kamto, Joseph; Qian, Lijun; Li, Wei; Han, ZhuTree multicast configuration of smart meters (SMs) can maintain the connectivity and meet the latency requirements for the Advanced Metering Infrastructure (AMI). However, such topology is extremely weak as any single failure suffices to break its connectivity. On the other hand, the impact of a SM node failure can be more or less significant: a noncut SM node will have a limited local impact compared to a cut SM node that will break the network connectivity. In this work, we design a highly connected tree with a set of backup links to minimize the weakness of tree topology of SMs. A topology repair scheme is proposed to address the impact of a SM node failure on the connectivity of the augmented tree network. It relies on a loop detection scheme to define the criticality of a SM node and specifically targets cut SM node by selecting backup parent SM to cover its children. Detailed algorithms to create such AMI tree and related theoretical and complexity analysis are provided with insightful simulation results: sufficient redundancy is provided to alleviate data loss at the cost of signaling overhead. It is however observed that biconnected tree provides the best compromise between the two entities.