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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Contributor
Date Range
2010 2019


Peer-to-peer systems are known to be vulnerable to the Sybil attack. The lack of a central authority allows a malicious user to create many fake identities (called Sybil nodes) pretending to be independent honest nodes. The goal of the malicious user is to influence the system on his/her behalf. In order to detect the Sybil nodes and prevent the attack, a reputation system is used for the nodes, built through observing its interactions with its peers. The construction makes every node a part of a distributed authority that keeps records on the reputation and behavior of the nodes. Records of …

Contributors
Cárdenas-Haro, José Antonio, Konjevod, Goran, Richa, Andrea W., et al.
Created Date
2010

Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for …

Contributors
Thulasiram, Ramesh L., Ye, Jieping, Xue, Guoliang, et al.
Created Date
2011

Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose …

Contributors
Chen, Jianhui, Ye, Jieping, Kumar, Sudhir, et al.
Created Date
2011

A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first part investigates stochastic optimization in real-time wireless systems, with the focus on the deadline-aware scheduling for real-time traffic. The optimal solution to such scheduling problems requires to explicitly taking into account the coupling in the deadline-aware transmissions and stochastic characteristics of the traffic, which involves a dynamic program that is …

Contributors
Yang, Lei, Zhang, Junshan, Tepedelenlioglu, Cihan, et al.
Created Date
2012

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 …

Contributors
Wang, Shanshan, Zhang, Junshan, Xue, Guoliang, et al.
Created Date
2012

Interference constitutes a major challenge for communication networks operating over a shared medium where availability is imperative. This dissertation studies the problem of designing and analyzing efficient medium access protocols which are robust against strong adversarial jamming. More specifically, four medium access (MAC) protocols (i.e., JADE, ANTIJAM, COMAC, and SINRMAC) which aim to achieve high throughput despite jamming activities under a variety of network and adversary models are presented. We also propose a self-stabilizing leader election protocol, SELECT, that can effectively elect a leader in the network with the existence of a strong adversary. Our protocols can not only deal …

Contributors
Zhang, Jin, Richa, Andrea W, Scheideler, Christian, et al.
Created Date
2012

Cognitive Radios (CR) are designed to dynamically reconfigure their transmission and/or reception parameters to utilize the bandwidth efficiently. With a rapidly fluctuating radio environment, spectrum management becomes crucial for cognitive radios. In a Cognitive Radio Ad Hoc Network (CRAHN) setting, the sensing and transmission times of the cognitive radio play a more important role because of the decentralized nature of the network. They have a direct impact on the throughput. Due to the tradeoff between throughput and the sensing time, finding optimal values for sensing time and transmission time is difficult. In this thesis, a method is proposed to improve …

Contributors
Bapat, Namrata, Syrotiuk, Violet R, Ahn, Gail-Joon, et al.
Created Date
2012

Semiconductor scaling technology has led to a sharp growth in transistor counts. This has resulted in an exponential increase on both power dissipation and heat flux (or power density) in modern microprocessors. These microprocessors are integrated as the major components in many modern embedded devices, which offer richer features and attain higher performance than ever before. Therefore, power and thermal management have become the significant design considerations for modern embedded devices. Dynamic voltage/frequency scaling (DVFS) and dynamic power management (DPM) are two well-known hardware capabilities offered by modern embedded processors. However, the power or thermal aware performance optimization is not …

Contributors
Zhang, Sushu, Chatha, Karam S, Cao, Yu, et al.
Created Date
2012

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 …

Contributors
Zhou, Shan, Ying, Lei, Zhang, Yanchao, et al.
Created Date
2013

While network problems have been addressed using a central administrative domain with a single objective, the devices in most networks are actually not owned by a single entity but by many individual entities. These entities make their decisions independently and selfishly, and maybe cooperate with a small group of other entities only when this form of coalition yields a better return. The interaction among multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives. In this dissertation, we are interested in modeling, analyzing, addressing network problems caused by the selfish behavior of network …

Contributors
Yang, Dejun, Xue, Guoliang, Richa, Andrea, et al.
Created Date
2013