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- All Subjects: Computer Science
- Genre: Academic theses
- Creators: Sen, Arunabha
Description
Today, many wireless networks are single-channel systems. However, as the interest in wireless services increases, the contention by nodes to occupy the medium is more intense and interference worsens. One direction with the potential to increase system throughput is multi-channel systems. Multi-channel systems have been shown to reduce collisions and increase concurrency thus producing better bandwidth usage. However, the well-known hidden- and exposed-terminal problems inherited from single-channel systems remain, and a new channel selection problem is introduced. In this dissertation, Multi-channel medium access control (MAC) protocols are proposed for mobile ad hoc networks (MANETs) for nodes equipped with a single half-duplex transceiver, using more sophisticated physical layer technologies. These include code division multiple access (CDMA), orthogonal frequency division multiple access (OFDMA), and diversity. CDMA increases channel reuse, while OFDMA enables communication by multiple users in parallel. There is a challenge to using each technology in MANETs, where there is no fixed infrastructure or centralized control. CDMA suffers from the near-far problem, while OFDMA requires channel synchronization to decode the signal. As a result CDMA and OFDMA are not yet widely used. Cooperative (diversity) mechanisms provide vital information to facilitate communication set-up between source-destination node pairs and help overcome limitations of physical layer technologies in MANETs. In this dissertation, the Cooperative CDMA-based Multi-channel MAC (CCM-MAC) protocol uses CDMA to enable concurrent transmissions on each channel. The Power-controlled CDMA-based Multi-channel MAC (PCC-MAC) protocol uses transmission power control at each node and mitigates collisions of control packets on the control channel by using different sizes of the spreading factor to have different processing gains for the control signals. The Cooperative Dual-access Multi-channel MAC (CDM-MAC) protocol combines the use of OFDMA and CDMA and minimizes channel interference by a resolvable balanced incomplete block design (BIBD). In each protocol, cooperating nodes help reduce the incidence of the multi-channel hidden- and exposed-terminal and help address the near-far problem of CDMA by supplying information. Simulation results show that each of the proposed protocols achieve significantly better system performance when compared to IEEE 802.11, other multi-channel protocols, and another protocol CDMA-based.
ContributorsMoon, Yuhan (Author) / Syrotiuk, Violet R. (Thesis advisor) / Huang, Dijiang (Committee member) / Reisslein, Martin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2010
Description
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 interactions between nodes are broadcast by the interacting nodes and honest reporting proves to be a Nash Equilibrium for correct (non-Sybil) nodes. In this research is argued that in realistic communication schedule scenarios, simple graph-theoretic queries such as the computation of Strongly Connected Components and Densest Subgraphs, help in exposing those nodes most likely to be Sybil, which are then proved to be Sybil or not through a direct test executed by some peers.
ContributorsCárdenas-Haro, José Antonio (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andréa W. (Thesis advisor) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2010
Description
Text search is a very useful way of retrieving document information from a particular website. The public generally use internet search engines over the local enterprise search engines, because the enterprise content is not cross linked and does not follow a page rank algorithm. On the other hand the enterprise search engine uses metadata information, which allows the user to specify the conditions that any retrieved document should meet. Therefore, using metadata information for searching will also be very useful. My thesis aims on developing an enterprise search engine using metadata information by providing advanced features like faceted navigation. The search engine data was extracted from various Indonesian web sources. Metadata information like person, organization, location, and sentiment analytic keyword entities should be tagged in each document to provide facet search capability. A shallow parsing technique like named entity recognizer is used for this purpose. There are more than 1500 entities that have been tagged in this process. These documents have been successfully converted into XML format and are indexed with "Apache Solr". It is an open source enterprise search engine with full text search and faceted search capabilities. The entities will be helpful for users to specify conditions and search faster through the large collection of documents. The user is assured results by clicking on a metadata condition. Since the sentiment analytic keywords are tagged with positive and negative values, social scientists can use these results to check for overlapping or conflicting organizations and ideologies. In addition, this tool is the first of its kind for the Indonesian language. The results are fetched much faster and with better accuracy.
ContributorsSanaka, Srinivasa Raviteja (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2010
Description
Realizing the applications of Internet of Things (IoT) with the goal of achieving a more efficient and automated world requires billions of connected smart devices and the minimization of hardware cost in these devices. As a result, many IoT devices do not have sufficient resources to support various protocols required in many IoT applications. Because of this, new protocols have been introduced to support the integration of these devices. One of these protocols is the increasingly popular routing protocol for low-power and lossy networks (RPL). However, this protocol is well known to attract blackhole and sinkhole attacks and cause serious difficulties when using more computationally intensive techniques to protect against these attacks, such as intrusion detection systems and rank authentication schemes. In this paper, an effective approach is presented to protect RPL networks against blackhole attacks. The approach does not address sinkhole attacks because they cause low damage and are often used along blackhole attacks and can be detected when blackhole attaches are detected. This approach uses the feature of multiple parents per node and a parent evaluation system enabling nodes to select more reliable routes. Simulations have been conducted, compared to existing approaches this approach would provide better protection against blackhole attacks with much lower overheads for small RPL networks.
ContributorsSanders, Kent (Author) / Yau, Stephen S (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2021
Description
Arc Routing Problems (ARPs) are a type of routing problem that finds routes of minimum total cost covering the edges or arcs in a graph representing street or road networks. They find application in many essential services such as residential waste collection, winter gritting, and others. Being NP-hard, solutions are usually found using heuristic methods. This dissertation contributes to heuristics for ARP, with a focus on the Capacitated Arc Routing Problem (CARP) with additional constraints. In operations such as residential waste collection, vehicle breakdown disruptions occur frequently. A new variant Capacitated Arc Re-routing Problem for Vehicle Break-down (CARP-VB) is introduced to address the need to re-route using only remaining vehicles to avoid missing services. A new heuristic Probe is developed to solve CARP-VB. Experiments on benchmark instances show that Probe is better in reducing the makespan and hence effective in reducing delays and avoiding missing services.
In addition to total cost, operators are also interested in solutions that are attractive, that is, routes that are contiguous, compact, and non-overlapping to manage the work. Operators may not adopt a solution that is not attractive even if it is optimum. They are also interested in solutions that are balanced in workload to meet equity requirements. A new multi-objective memetic algorithm, MA-ABC is developed, that optimizes three objectives: Attractiveness, makespan, and total cost. On testing with benchmark instances, MA-ABC was found to be effective in providing attractive and balanced route solutions without affecting the total cost.
Changes in the problem specification such as demand and topology occurs frequently in business operations. Machine learning be applied to learn the distribution behind these changes and generate solutions quickly at time of inference. Splice is a machine learning framework for CARP that generates closer to optimum solutions quickly using a graph neural network and deep Q-learning. Splice can solve several variants of node and arc routing problems using the same architecture without any modification. Splice was trained and tested using randomly generated instances. Splice generated solutions faster that are also better in comparison to popular metaheuristics.
ContributorsRamamoorthy, Muhilan (Author) / Syrotiuk, Violet R. (Thesis advisor) / Forrest, Stephanie (Committee member) / Mirchandani, Pitu (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2022
Description
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform.However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform. In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves re-weighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.
ContributorsKarami, Mansooreh (Author) / Liu, Huan (Thesis advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Mancenido, Michelle V. (Committee member) / Arizona State University (Publisher)
Created2023
Description
The problem of monitoring complex networks for the detection of anomalous behavior is well known. Sensors are usually deployed for the purpose of monitoring these networks for anomalies and Sensor Placement Optimization (SPO) is the problem of determining where these sensors should be placed (deployed) in the network. Prior works have utilized the well known Set Cover formulation in order to determine the locations where sensors should be placed in the network, so that anomalies can be effectively detected. However, such works cannot be utilized to address the problem when the objective is to not only detect the presence of anomalies, but also to detect (distinguish) the source(s) of the detected anomalies, i.e., uniquely monitoring the network. In this dissertation, I attempt to fill in this gap by utilizing the mathematical concept of Identifying Codes and illustrating how it not only can overcome the aforementioned limitation, but also it, and its variants, can be utilized to monitor complex networks modeled from multiple domains. Over the course of this dissertation, I make key contributions which further enhance the efficacy and applicability of Identifying Codes as a monitoring strategy. First, I show how Identifying Codes are superior to not only the Set Cover formulation but also standard graph centrality metrics, for the purpose of uniquely monitoring complex networks. Second, I study novel problems such as the budget constrained Identifying Code, scalable Identifying Code, robust Identifying Code etc., and present algorithms and results for the respective problems. Third, I present useful Identifying Code results for restricted graph classes such as Unit Interval Bigraphs and Unit Disc Bigraphs. Finally, I show the universality of Identifying Codes by applying it to multiple domains.
ContributorsBasu, Kaustav (Author) / Sen, Arunabha (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2022
Description
Water, energy, and food are essential resources to sustain the development of the society. The Food-Energy-Water Nexus (FEW-Nexus) must account for synergies and trade-offs among these resources. The nexus concept highlights the importance of integrative solutions that secure supplies to meet demands sustainably. The existing frameworks and tools do not focus on formal model composability, a key capability for creating simulations created from separately developed models. The Knowledge Interchange Broker (KIB) approach is used to model the interactions among models to achieve composition flexibility for the FEW-Nexus.Domain experts generally use the Water Evaluation and Planning (WEAP) and Low Emissions Analysis Platform (LEAP) systems to study water and energy systems, respectively. The food part of FEW systems can be modeled inside the WEAP system. An internal linkage mechanism is available for combining and simulating WEAP and LEAP models. This mechanism is used for the validation and performance evaluation of independent modeling and simulation proposed in this research. The Componentized WEAP and LEAP RESTful frameworks are component-based representations for the legacy and closed-source WEAP and LEAP systems. These modularized systems simplify their use with other simulation frameworks.
This research proposes two interaction model frameworks based on the Knowledge Interchange Broker approach. First, an Algorithmic Interaction Model (Algorithmic-IM) was developed to integrate the WEAP and LEAP models. The Algorithmic-IM model can be defined via programming language and has a fixed cyclic execution protocol. However, this approach has tightly interwoven the interaction model with its execution and has limited support for flexibly creating model hierarchies. To overcome these restrictions, the system-theoretic Parallel DEVS formalism is used to develop a DEVS-Based Interaction Model (DEVS-IM). As in the Algorithmic-IM, the DEVS-IM is implemented as a RESTful framework, uses MongoDB for defining structural DEVS models, and supports automatic code generation for the DEVSSuite
simulator. The DEVS-IM offers modular, hierarchical structural modeling, reusability, flexibility, and maintainability for integrating disparate systems.
The Phoenix Active Management Area (AMA) is used to demonstrate the real-world application of the proposed research. Furthermore, the correctness and performance of the presented frameworks in this research are evaluated using the Phoenix-AMA model.
ContributorsFard, Mostafa D (Author) / Sarjoughian, Hessam S (Thesis advisor) / Barton, Michael (Committee member) / Sen, Arunabha (Committee member) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2023
Description
Quantum computing holds the potential to revolutionize various industries by solving problems that classical computers cannot solve efficiently. However, building quantum computers is still in its infancy, and simulators are currently the best available option to explore the potential of quantum computing. Therefore, developing comprehensive benchmarking suites for quantum computing simulators is essential to evaluate their performance and guide the development of future quantum algorithms and hardware. This study presents a systematic evaluation of quantum computing simulators’ performance using a benchmarking suite. The benchmarking suite is designed to meet the industry-standard performance benchmarks established by the Defense Advanced Research Projects Agency (DARPA) and includes standardized test data and comparison metrics that encompass a wide range of applications, deep neural network models, and optimization techniques. The thesis is divided into two parts to cover basic quantum algorithms and variational quantum algorithms for practical machine-learning tasks. In the first part, the run time and memory performance of quantum computing simulators are analyzed using basic quantum algorithms. The performance is evaluated using standardized test data and comparison metrics that cover fundamental quantum algorithms, including Quantum Fourier Transform (QFT), Inverse Quantum Fourier Transform (IQFT), Quantum Adder, and Variational Quantum Eigensolver (VQE). The analysis provides valuable insights into the simulators’ strengths and weaknesses and highlights the need for further development to enhance their performance. In the second part, benchmarks are developed using variational quantum algorithms for practical machine learning tasks such as image classification, natural language processing, and recommendation. The benchmarks address several unique challenges posed by benchmarking quantum machine learning (QML), including the effect of optimizations on time-to-solution, the stochastic nature of training, the inclusion of hybrid quantum-classical layers, and the diversity of software and hardware systems. The findings offer valuable insights into the simulators’ ability to solve practical machine-learning tasks and pinpoint areas for future research and enhancement. In conclusion, this study provides a rigorous evaluation of quantum computing simulators’ performance using a benchmarking suite that meets industry-standard performance benchmarks.
ContributorsSathyakumar, Rajesh (Author) / Spanias, Andreas (Thesis advisor) / Sen, Arunabha (Thesis advisor) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2023
Description
There are many applications where the truth is unknown. The truth values are
guessed by different sources. The values of different properties can be obtained from
various sources. These will lead to the disagreement in sources. An important task
is to obtain the truth from these sometimes contradictory sources. In the extension
of computing the truth, the reliability of sources needs to be computed. There are
models which compute the precision values. In those earlier models Banerjee et al.
(2005) Dong and Naumann (2009) Kasneci et al. (2011) Li et al. (2012) Marian and
Wu (2011) Zhao and Han (2012) Zhao et al. (2012), multiple properties are modeled
individually. In one of the existing works, the heterogeneous properties are modeled in
a joined way. In that work, the framework i.e. Conflict Resolution on Heterogeneous
Data (CRH) framework is based on the single objective optimization. Due to the
single objective optimization and non-convex optimization problem, only one local
optimal solution is found. As this is a non-convex optimization problem, the optimal
point depends upon the initial point. This single objective optimization problem is
converted into a multi-objective optimization problem. Due to the multi-objective
optimization problem, the Pareto optimal points are computed. In an extension of
that, the single objective optimization problem is solved with numerous initial points.
The above two approaches are used for finding the solution better than the solution
obtained in the CRH with median as the initial point for the continuous variables and
majority voting as the initial point for the categorical variables. In the experiments,
the solution, coming from the CRH, lies in the Pareto optimal points of the multiobjective
optimization and the solution coming from the CRH is the optimum solution
in these experiments.
guessed by different sources. The values of different properties can be obtained from
various sources. These will lead to the disagreement in sources. An important task
is to obtain the truth from these sometimes contradictory sources. In the extension
of computing the truth, the reliability of sources needs to be computed. There are
models which compute the precision values. In those earlier models Banerjee et al.
(2005) Dong and Naumann (2009) Kasneci et al. (2011) Li et al. (2012) Marian and
Wu (2011) Zhao and Han (2012) Zhao et al. (2012), multiple properties are modeled
individually. In one of the existing works, the heterogeneous properties are modeled in
a joined way. In that work, the framework i.e. Conflict Resolution on Heterogeneous
Data (CRH) framework is based on the single objective optimization. Due to the
single objective optimization and non-convex optimization problem, only one local
optimal solution is found. As this is a non-convex optimization problem, the optimal
point depends upon the initial point. This single objective optimization problem is
converted into a multi-objective optimization problem. Due to the multi-objective
optimization problem, the Pareto optimal points are computed. In an extension of
that, the single objective optimization problem is solved with numerous initial points.
The above two approaches are used for finding the solution better than the solution
obtained in the CRH with median as the initial point for the continuous variables and
majority voting as the initial point for the categorical variables. In the experiments,
the solution, coming from the CRH, lies in the Pareto optimal points of the multiobjective
optimization and the solution coming from the CRH is the optimum solution
in these experiments.
ContributorsJain, Karan (Author) / Xue, Guoliang (Thesis advisor) / Sen, Arunabha (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2019