Matching Items (82)
Description
A principal goal of this dissertation is to study wireless network design and optimization with the focus on two perspectives: 1) socially-aware mobile networking and computing; 2) security and privacy in wireless networking. Under this common theme, this dissertation can be broadly organized into three parts.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.
ContributorsGong, Xiaowen (Author) / Zhang, Junshan (Thesis advisor) / Cochran, Douglas (Committee member) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
Modern systems that measure dynamical phenomena often have limitations as to how many sensors can operate at any given time step. This thesis considers a sensor scheduling problem in which the source of a diffusive phenomenon is to be localized using single point measurements of its concentration. With a linear diffusion model, and in the absence of noise, classical observability theory describes whether or not the system's initial state can be deduced from a given set of linear measurements. However, it does not describe to what degree the system is observable. Different metrics of observability have been proposed in literature to address this issue. Many of these methods are based on choosing optimal or sub-optimal sensor schedules from a predetermined collection of possibilities. This thesis proposes two greedy algorithms for a one-dimensional and two-dimensional discrete diffusion processes. The first algorithm considers a deterministic linear dynamical system and deterministic linear measurements. The second algorithm considers noise on the measurements and is compared to a Kalman filter scheduling method described in published work.
ContributorsNajam, Anbar (Author) / Cochran, Douglas (Thesis advisor) / Turaga, Pavan (Committee member) / Wang, Chao (Committee member) / Arizona State University (Publisher)
Created2016
Description
The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.
ContributorsAnirudh, Rushil (Author) / Turaga, Pavan (Thesis advisor) / Cochran, Douglas (Committee member) / Runger, George C. (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2016
Description
Resource allocation in communication networks aims to assign various resources such as power, bandwidth and load in a fair and economic fashion so that the networks can be better utilized and shared by the communicating entities. The design of efficient resource-allocation algorithms is, however, becoming more and more challenging due to the precipitously increasing scale of the networks. This thesis strives to understand how to design such low-complexity algorithms with performance guarantees.
In the first part, the link scheduling problem in wireless ad hoc networks is considered. The scheduler is charge of finding a set of wireless data links to activate at each time slot with the considerations of wireless interference, traffic dynamics, network topology and quality-of-service (QoS) requirements. Two different yet essential scenarios are investigated: the first one is when each packet has a specific deadline after which it will be discarded; the second is when each packet traverses the network in multiple hops instead of leaving the network after a one-hop transmission. In both scenarios the links need to be carefully scheduled to avoid starvation of users and congestion on links. One greedy algorithm is analyzed in each of the two scenarios and performance guarantees in terms of throughput of the networks are derived.
In the second part, the load-balancing problem in parallel computing is studied. Tasks arrive in batches and the duty of the load balancer is to place the tasks on the machines such that minimum queueing delay is incurred. Due to the huge size of modern data centers, sampling the status of all machines may result in significant overhead. Consequently, an algorithm based on limited queue information at the machines is examined and its asymptotic delay performance is characterized and it is shown that the proposed algorithm achieves the same delay with remarkably less sampling overhead compared to the well-known power-of-two-choices algorithm.
Two messages of the thesis are the following: greedy algorithms can work well in a stochastic setting; the fluid model can be useful in "derandomizing" the system and reveal the nature of the algorithm.
In the first part, the link scheduling problem in wireless ad hoc networks is considered. The scheduler is charge of finding a set of wireless data links to activate at each time slot with the considerations of wireless interference, traffic dynamics, network topology and quality-of-service (QoS) requirements. Two different yet essential scenarios are investigated: the first one is when each packet has a specific deadline after which it will be discarded; the second is when each packet traverses the network in multiple hops instead of leaving the network after a one-hop transmission. In both scenarios the links need to be carefully scheduled to avoid starvation of users and congestion on links. One greedy algorithm is analyzed in each of the two scenarios and performance guarantees in terms of throughput of the networks are derived.
In the second part, the load-balancing problem in parallel computing is studied. Tasks arrive in batches and the duty of the load balancer is to place the tasks on the machines such that minimum queueing delay is incurred. Due to the huge size of modern data centers, sampling the status of all machines may result in significant overhead. Consequently, an algorithm based on limited queue information at the machines is examined and its asymptotic delay performance is characterized and it is shown that the proposed algorithm achieves the same delay with remarkably less sampling overhead compared to the well-known power-of-two-choices algorithm.
Two messages of the thesis are the following: greedy algorithms can work well in a stochastic setting; the fluid model can be useful in "derandomizing" the system and reveal the nature of the algorithm.
ContributorsKang, Xiaohan (Author) / Ying, Lei (Thesis advisor) / Cochran, Douglas (Committee member) / Dai, Jim (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2015
Description
RF convergence of radar and communications users is rapidly becoming an issue for a multitude of stakeholders. To hedge against growing spectral congestion, research into cooperative radar and communications systems has been identified as a critical necessity for the United States and other countries. Further, the joint sensing-communicating paradigm appears imminent in several technological domains. In the pursuit of co-designing radar and communications systems that work cooperatively and benefit from each other's existence, joint radar-communications metrics are defined and bounded as a measure of performance. Estimation rate is introduced, a novel measure of radar estimation information as a function of time. Complementary to communications data rate, the two systems can now be compared on the same scale. An information-centric approach has a number of advantages, defining precisely what is gained through radar illumination and serves as a measure of spectral efficiency. Bounding radar estimation rate and communications data rate jointly, systems can be designed as a joint optimization problem.
ContributorsPaul, Bryan (Author) / Bliss, Daniel W. (Thesis advisor) / Berisha, Visar (Committee member) / Kosut, Oliver (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2017
Description
There is an ever-growing need for broadband conformal antennas to not only reduce the number of antennas utilized to cover a broad range of frequencies (VHF-UHF) but also to reduce visual and RF signatures associated with communication systems. In many applications antennas needs to be very close to low-impedance mediums or embedded inside low-impedance mediums. However, for conventional metal and dielectric antennas to operate efficiently in such environments either a very narrow bandwidth must be tolerated, or enough loss added to expand the bandwidth, or they must be placed one quarter of a wavelength above the conducting surface. The latter is not always possible since in the HF through low UHF bands, critical to Military and Security functions, this quarter-wavelength requirement would result in impractically large antennas.
Despite an error based on a false assumption in the 1950’s, which had severely underestimated the efficiency of magneto-dielectric antennas, recently demonstrated magnetic-antennas have been shown to exhibit extraordinary efficiency in conformal applications. Whereas conventional metal-and-dielectric antennas carrying radiating electric currents suffer a significant disadvantage when placed conformal to the conducting surface of a platform, because they induce opposing image currents in the surface, magnetic-antennas carrying magnetic radiating currents have no such limitation. Their magnetic currents produce co-linear image currents in electrically conducting surfaces.
However, the permeable antennas built to date have not yet attained the wide bandwidth expected because the magnetic-flux-channels carrying the wave have not been designed to guide the wave near the speed of light at all frequencies. Instead, they tend to lose the wave by a leaky fast-wave mechanism at low frequencies or they over-bind a slow-wave at high frequencies. In this dissertation, we have studied magnetic antennas in detail and presented the design approach and apparatus required to implement a flux-channel carrying the magnetic current wave near the speed of light over a very broad frequency range which also makes the design of a frequency independent antenna (spiral) possible. We will learn how to construct extremely thin conformal antennas, frequency-independent permeable antennas, and even micron-sized antennas that can be embedded inside the brain without damaging the tissue.
Despite an error based on a false assumption in the 1950’s, which had severely underestimated the efficiency of magneto-dielectric antennas, recently demonstrated magnetic-antennas have been shown to exhibit extraordinary efficiency in conformal applications. Whereas conventional metal-and-dielectric antennas carrying radiating electric currents suffer a significant disadvantage when placed conformal to the conducting surface of a platform, because they induce opposing image currents in the surface, magnetic-antennas carrying magnetic radiating currents have no such limitation. Their magnetic currents produce co-linear image currents in electrically conducting surfaces.
However, the permeable antennas built to date have not yet attained the wide bandwidth expected because the magnetic-flux-channels carrying the wave have not been designed to guide the wave near the speed of light at all frequencies. Instead, they tend to lose the wave by a leaky fast-wave mechanism at low frequencies or they over-bind a slow-wave at high frequencies. In this dissertation, we have studied magnetic antennas in detail and presented the design approach and apparatus required to implement a flux-channel carrying the magnetic current wave near the speed of light over a very broad frequency range which also makes the design of a frequency independent antenna (spiral) possible. We will learn how to construct extremely thin conformal antennas, frequency-independent permeable antennas, and even micron-sized antennas that can be embedded inside the brain without damaging the tissue.
ContributorsYousefi, Tara (Author) / Diaz, Rodolfo E (Thesis advisor) / Cochran, Douglas (Committee member) / Goodnick, Stephen (Committee member) / Pan, George (Committee member) / Arizona State University (Publisher)
Created2017
Description
Electric field imaging allows for a low cost, compact, non-invasive, non-ionizing alternative to other methods of imaging. It has many promising industrial applications including security, safely imaging power lines at construction sites, finding sources of electromagnetic interference, geo-prospecting, and medical imaging. The work presented in this dissertation concerns low frequency electric field imaging: the physics, hardware, and various methods of achieving it.
Electric fields have historically been notoriously difficult to work with due to how intrinsically noisy the data is in electric field sensors. As a first contribution, an in-depth study demonstrates just how prevalent electric field noise is. In field tests, various cables were placed underneath power lines. Despite being shielded, the 60 Hz power line signal readily penetrated several types of cables.
The challenges of high noise levels were largely addressed by connecting the output of an electric field sensor to a lock-in amplifier. Using the more accurate means of collecting electric field data, D-dot sensors were arrayed in a compact grid to resolve electric field images as a second contribution. This imager has successfully captured electric field images of live concealed wires and electromagnetic interference.
An active method was developed as a third contribution. In this method, distortions created by objects when placed in a known electric field are read. This expands the domain of what can be imaged because the object does not need to be a time-varying electric field source. Images of dielectrics (e.g. bodies of water) and DC wires were captured using this new method.
The final contribution uses a collection of one-dimensional electric field images, i.e. projections, to reconstruct a two-dimensional image. This was achieved using algorithms based in computed tomography such as filtered backprojection. An algebraic approach was also used to enforce sparsity regularization with the L1 norm, further improving the quality of some images.
Electric fields have historically been notoriously difficult to work with due to how intrinsically noisy the data is in electric field sensors. As a first contribution, an in-depth study demonstrates just how prevalent electric field noise is. In field tests, various cables were placed underneath power lines. Despite being shielded, the 60 Hz power line signal readily penetrated several types of cables.
The challenges of high noise levels were largely addressed by connecting the output of an electric field sensor to a lock-in amplifier. Using the more accurate means of collecting electric field data, D-dot sensors were arrayed in a compact grid to resolve electric field images as a second contribution. This imager has successfully captured electric field images of live concealed wires and electromagnetic interference.
An active method was developed as a third contribution. In this method, distortions created by objects when placed in a known electric field are read. This expands the domain of what can be imaged because the object does not need to be a time-varying electric field source. Images of dielectrics (e.g. bodies of water) and DC wires were captured using this new method.
The final contribution uses a collection of one-dimensional electric field images, i.e. projections, to reconstruct a two-dimensional image. This was achieved using algorithms based in computed tomography such as filtered backprojection. An algebraic approach was also used to enforce sparsity regularization with the L1 norm, further improving the quality of some images.
ContributorsChung, Hugh Emanuel (Author) / Allee, David R. (Thesis advisor) / Cochran, Douglas (Committee member) / Aberle, James T (Committee member) / Phillips, Stephen M (Committee member) / Arizona State University (Publisher)
Created2017
Description
In this dissertation, I propose potential techniques to improve the quality-of-service (QoS) of real-time applications in cognitive radio (CR) systems. Unlike best-effort applications, real-time applications, such as audio and video, have a QoS that need to be met. There are two different frameworks that are used to study the QoS in the literature, namely, the average-delay and the hard-deadline frameworks. In the former, the scheduling algorithm has to guarantee that the packet's average delay is below a prespecified threshold while the latter imposes a hard deadline on each packet in the system. In this dissertation, I present joint power allocation and scheduling algorithms for each framework and show their applications in CR systems which are known to have strict power limitations so as to protect the licensed users from interference.
A common aspect of the two frameworks is the packet service time. Thus, the effect of multiple channels on the service time is studied first. The problem is formulated as an optimal stopping rule problem where it is required to decide at which channel the SU should stop sensing and begin transmission. I provide a closed-form expression for this optimal stopping rule and the optimal transmission power of secondary user (SU).
The average-delay framework is then presented in a single CR channel system with a base station (BS) that schedules the SUs to minimize the average delay while protecting the primary users (PUs) from harmful interference. One of the contributions of the proposed algorithm is its suitability for heterogeneous-channels systems where users with statistically low channel quality suffer worse delay performances. The proposed algorithm guarantees the prespecified delay performance to each SU without violating the PU's interference constraint.
Finally, in the hard-deadline framework, I propose three algorithms that maximize the system's throughput while guaranteeing the required percentage of packets to be transmitted by their deadlines. The proposed algorithms work in heterogeneous systems where the BS is serving different types of users having real-time (RT) data and non-real-time (NRT) data. I show that two of the proposed algorithms have the low complexity where the power policies of both the RT and NRT users are in closed-form expressions and a low-complexity scheduler.
A common aspect of the two frameworks is the packet service time. Thus, the effect of multiple channels on the service time is studied first. The problem is formulated as an optimal stopping rule problem where it is required to decide at which channel the SU should stop sensing and begin transmission. I provide a closed-form expression for this optimal stopping rule and the optimal transmission power of secondary user (SU).
The average-delay framework is then presented in a single CR channel system with a base station (BS) that schedules the SUs to minimize the average delay while protecting the primary users (PUs) from harmful interference. One of the contributions of the proposed algorithm is its suitability for heterogeneous-channels systems where users with statistically low channel quality suffer worse delay performances. The proposed algorithm guarantees the prespecified delay performance to each SU without violating the PU's interference constraint.
Finally, in the hard-deadline framework, I propose three algorithms that maximize the system's throughput while guaranteeing the required percentage of packets to be transmitted by their deadlines. The proposed algorithms work in heterogeneous systems where the BS is serving different types of users having real-time (RT) data and non-real-time (NRT) data. I show that two of the proposed algorithms have the low complexity where the power policies of both the RT and NRT users are in closed-form expressions and a low-complexity scheduler.
ContributorsEwaisha, Ahmed Emad (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Ying, Lei (Committee member) / Bliss, Daniel (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2016
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
The problem of detecting the presence of a known signal in multiple channels of additive white Gaussian noise, such as occurs in active radar with a single transmitter and multiple geographically distributed receivers, is addressed via coherent multiple-channel techniques. A replica of the transmitted signal replica is treated as a one channel in a M-channel detector with the remaining M-1 channels comprised of data from the receivers. It is shown that the distribution of the eigenvalues of a Gram matrix are invariant to the presence of the signal replica on one channel provided the other M-1 channels are independent and contain only white Gaussian noise. Thus, the thresholds representing false alarm probabilities for detectors based on functions of these eigenvalues remain valid when one channel is known to not contain only noise. The derivation is supported by results from Monte Carlo simulations. The performance of the largest eigenvalue as a detection statistic in the active case is examined, and compared to the normalized matched filter detector in a two and three channel case.
ContributorsBeaudet, Kaitlyn Elizabeth (Author) / Cochran, Douglas (Thesis director) / Wu, Teresa (Committee member) / Howard, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2013-05