Matching Items (42)
Description
With the increasing user demand for low latency, elastic provisioning of computing resources coupled with ubiquitous and on-demand access to real-time data, cloud computing has emerged as a popular computing paradigm to meet growing user demands. However, with the introduction and rising use of wear- able technology and evolving uses of smart-phones, the concept of Internet of Things (IoT) has become a prevailing notion in the currently growing technology industry. Cisco Inc. has projected a data creation of approximately 403 Zetabytes (ZB) by 2018. The combination of bringing benign devices and connecting them to the web has resulted in exploding service and data aggregation requirements, thus requiring a new and innovative computing platform. This platform should have the capability to provide robust real-time data analytics and resource provisioning to clients, such as IoT users, on-demand. Such a computation model would need to function at the edge-of-the-network, forming a bridge between the large cloud data centers and the distributed connected devices.
This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications.
This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications.
ContributorsDsouza, Clinton (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2015
Description
Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios.
ContributorsPonneganti, Ramu (Author) / Yau, Stephen (Thesis advisor) / Richa, Andrea (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
Description
As integrated technologies are scaling down, there is an increasing trend in the
process,voltage and temperature (PVT) variations of highly integrated RF systems.
Accounting for these variations during the design phase requires tremendous amount
of time for prediction of RF performance and optimizing it accordingly. Thus, there
is an increasing gap between the need to relax the RF performance requirements at
the design phase for rapid development and the need to provide high performance
and low cost RF circuits that function with PVT variations. No matter how care-
fully designed, RF integrated circuits (ICs) manufactured with advanced technology
nodes necessitate lengthy post-production calibration and test cycles with expensive
RF test instruments. Hence design-for-test (DFT) is proposed for low-cost and fast
measurement of performance parameters during both post-production and in-eld op-
eration. For example, built-in self-test (BIST) is a DFT solution for low-cost on-chip
measurement of RF performance parameters. In this dissertation, three aspects of
automated test and calibration, including DFT mathematical model, BIST hardware
and built-in calibration are covered for RF front-end blocks.
First, the theoretical foundation of a post-production test of RF integrated phased
array antennas is proposed by developing the mathematical model to measure gain
and phase mismatches between antenna elements without any electrical contact. The
proposed technique is fast, cost-efficient and uses near-field measurement of radiated
power from antennas hence, it requires single test setup, it has easy implementation
and it is short in time which makes it viable for industrialized high volume integrated
IC production test.
Second, a BIST model intended for the characterization of I/Q offset, gain and
phase mismatch of IQ transmitters without relying on external equipment is intro-
duced. The proposed BIST method is based on on-chip amplitude measurement as
in prior works however,here the variations in the BIST circuit do not affect the target
parameter estimation accuracy since measurements are designed to be relative. The
BIST circuit is implemented in 130nm technology and can be used for post-production
and in-field calibration.
Third, a programmable low noise amplifier (LNA) is proposed which is adaptable
to different application scenarios depending on the specification requirements. Its
performance is optimized with regards to required specifications e.g. distance, power
consumption, BER, data rate, etc.The statistical modeling is used to capture the
correlations among measured performance parameters and calibration modes for fast
adaptation. Machine learning technique is used to capture these non-linear correlations and build the probability distribution of a target parameter based on measurement results of the correlated parameters. The proposed concept is demonstrated by
embedding built-in tuning knobs in LNA design in 130nm technology. The tuning
knobs are carefully designed to provide independent combinations of important per-
formance parameters such as gain and linearity. Minimum number of switches are
used to provide the desired tuning range without a need for an external analog input.
process,voltage and temperature (PVT) variations of highly integrated RF systems.
Accounting for these variations during the design phase requires tremendous amount
of time for prediction of RF performance and optimizing it accordingly. Thus, there
is an increasing gap between the need to relax the RF performance requirements at
the design phase for rapid development and the need to provide high performance
and low cost RF circuits that function with PVT variations. No matter how care-
fully designed, RF integrated circuits (ICs) manufactured with advanced technology
nodes necessitate lengthy post-production calibration and test cycles with expensive
RF test instruments. Hence design-for-test (DFT) is proposed for low-cost and fast
measurement of performance parameters during both post-production and in-eld op-
eration. For example, built-in self-test (BIST) is a DFT solution for low-cost on-chip
measurement of RF performance parameters. In this dissertation, three aspects of
automated test and calibration, including DFT mathematical model, BIST hardware
and built-in calibration are covered for RF front-end blocks.
First, the theoretical foundation of a post-production test of RF integrated phased
array antennas is proposed by developing the mathematical model to measure gain
and phase mismatches between antenna elements without any electrical contact. The
proposed technique is fast, cost-efficient and uses near-field measurement of radiated
power from antennas hence, it requires single test setup, it has easy implementation
and it is short in time which makes it viable for industrialized high volume integrated
IC production test.
Second, a BIST model intended for the characterization of I/Q offset, gain and
phase mismatch of IQ transmitters without relying on external equipment is intro-
duced. The proposed BIST method is based on on-chip amplitude measurement as
in prior works however,here the variations in the BIST circuit do not affect the target
parameter estimation accuracy since measurements are designed to be relative. The
BIST circuit is implemented in 130nm technology and can be used for post-production
and in-field calibration.
Third, a programmable low noise amplifier (LNA) is proposed which is adaptable
to different application scenarios depending on the specification requirements. Its
performance is optimized with regards to required specifications e.g. distance, power
consumption, BER, data rate, etc.The statistical modeling is used to capture the
correlations among measured performance parameters and calibration modes for fast
adaptation. Machine learning technique is used to capture these non-linear correlations and build the probability distribution of a target parameter based on measurement results of the correlated parameters. The proposed concept is demonstrated by
embedding built-in tuning knobs in LNA design in 130nm technology. The tuning
knobs are carefully designed to provide independent combinations of important per-
formance parameters such as gain and linearity. Minimum number of switches are
used to provide the desired tuning range without a need for an external analog input.
ContributorsShafiee, Maryam (Author) / Ozev, Sule (Thesis advisor) / Diaz, Rodolfo (Committee member) / Ogras, Umit Y. (Committee member) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2018
Description
Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.
ContributorsDe La Rosa, Matthew Lee (Author) / Chen, Yinong (Thesis advisor) / Collofello, James (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2018
Description
For our collaborative thesis we explored the US electric utility market and how the Internet of Things technology movement could capture a possible advancement of the current existing grid. Our objective of this project was to successfully understand the market trends in the utility space and identify where a semiconductor manufacturing company, with a focus on IoT technology, could penetrate the market using their products. The methodology used for our research was to conduct industry interviews to formulate common trends in the utility and industrial hardware manufacturer industries. From there, we composed various strategies that The Company should explore. These strategies were backed up using qualitative reasoning and forecasted discounted cash flow and net present value analysis. We confirmed that The Company should use specific silicon microprocessors and microcontrollers that pertained to each of the four devices analytics demand. Along with a silicon strategy, our group believes that there is a strong argument for a data analytics software package by forming strategic partnerships in this space.
ContributorsLlazani, Loris (Co-author) / Ruland, Matthew (Co-author) / Medl, Jordan (Co-author) / Crowe, David (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Economics (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Information Systems (Contributor) / Hugh Downs School of Human Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
As the IoT (Internet of Things) market continues to grow, Company X needs to find a way to penetrate the market and establish larger market share. The problem with Company X's current strategy and cost structure lies in the fact that the fastest growing portion of the IoT market is microcontrollers (MCUs). As Company X currently holds its focus in manufacturing microprocessors (MPUs), the current manufacturing strategy is not optimal for entering competitively into the MCU space. Within the MCU space, the companies that are competing the best do not utilize such high level manufacturing processes because these low cost products do not demand them. Given that the MCU market is largely untested by Company X and its products would need to be manufactured at increasingly lower costs, it runs the risk of over producing and holding obsolete inventory that is either scrapped or sold at or below cost. In order to eliminate that risk, we will explore alternative manufacturing strategies for Company X's MCU products specifically, which will allow for a more optimal cost structure and ultimately a more profitable Internet of Things Group (IoTG). The IoT MCU ecosystem does not require the high powered technology Company X is currently manufacturing and therefore, Company X loses large margins due to its unnecessary leading technology. Since cash is king, pursuing a fully external model for MCU design and manufacturing processes will generate the highest NPV for Company X. It also will increase Company X's market share, which is extremely important given that every tech company in the world is trying to get its hands into the IoT market. It is possible that in ten to thirty years down the road, Company X can manufacture enough units to keep its products in-house, but this is not feasible in the foreseeable future. For now, Company X should focus on the cost market of MCUs by driving its prices down while maintaining low costs due to the variables of COGS and R&D given in our fully external strategy.
ContributorsKadi, Bengimen (Co-author) / Peterson, Tyler (Co-author) / Langmack, Haley (Co-author) / Quintana, Vince (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / School of Accountancy (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The purpose of this thesis was to design a market entrance strategy for Company X to enter the microcontroller (MCU) market within the Internet of Things (IoT). The five IoT segments are automotive; medical; retail; industrial; and military, aerospace, and government. To reach a final decision, we will research the markets, analyze make versus buy scenarios, and deliver a financial analysis on the chosen strategy. Based on the potential financial benefits and compatibility with Company X's current business model, we recommend that Company X enter the automotive segment through mergers & acquisitions (M&A). After analyzing the supply chain structure of the automotive IoT, we advise Company X to acquire Freescale Semiconductor for $46.98 per share.
ContributorsBradley, Rachel (Co-author) / Fankhauser, Elisa (Co-author) / McCoach, Robert (Co-author) / Zheng, Weilin (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / School of Accountancy (Contributor) / School of International Letters and Cultures (Contributor) / WPC Graduate Programs (Contributor)
Created2015-05
Description
IoT Media broadcast devices, such as the Roku stick, Amazon Fire, and Chromecast have been emerging onto the market recently as a portable and inexpensive alternative to cable and disk players, allowing easy integration between home and business Wi-Fi networks and television systems capable of supporting HDMI inputs without the additional overhead of setting up a heavy or complicated player or computer. The rapid expansion of these products as a mechanism to provide for TV Everywhere services for entertainment as well as cheap office appliances brings yet another node in the rapidly expanding network of IoT that surrounds us today. However, the security implications of these devices are nearly unexplored, despite their prevalence. In this thesis, I will go over the structure and mechanisms of Chromecast, and explore some of the potential exploits and consequences of the device. The thesis contains an overview of the inner workings of Chromecast, goes over the segregation and limited control and fundamental design choices of the Android based OS. It then identifies the objectives of security, four different potential methods of exploit to compromise those objectives on a Chromecast and/or its attached network, including rogue applications, traffic sniffing, evil access points and the most effective one: deauthentication attack. Tests or relevant analysis were carried out for each of these methods, and conclusions were drawn on their effectiveness. There is then a conclusion revolving around the consequences, mitigation and the future implications of security issues on Chromecast and the larger IoT landscape.
ContributorsHuang, Kaiyi (Author) / Zhao, Ziming (Thesis director) / Ahn, Gail-Joon (Committee member) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The basis of this project was to analyze the potential cost savings derived from the implementation of an ultrasonic flaw detector for gas pipes in factories. The group began by researching the market of the Industrial Internet of Things. IIoT is a very attractive market for investment, as connected technologies are become both more advanced and more affordable. Factory automation also saves costs of human capital, maintenance, and bad product cost as well as safety. After doing this preliminary research, the group continued by identifying potential solutions to current shortcomings of the manufacturing status quo. After narrowing down the options, the ultrasonic flaw detector appeared to have the highest potential for success in Company X's factories. The group began doing research on what physical components would go into this solution. They found pricing for all of the various parts of such a device as well as estimated labor, maintenance, and implementation costs. After estimating these costs, the team began the construction of a detailed financial model to generate the hypothetical net present value of such a tool. After presenting two times to a panel of Company X employees, the group decided to focus only on cost savings for Company X, and not the potential revenues of selling the whole solution. They ran a sensitivity analysis on all of the factors that contributed to the NPV of the project, and discovered that the estimated percentage of scrapped product resulting from gas leaks and the percentage of gas lost to leaks contributed the most to the NPV.
ContributorsFlick, Jacob (Co-author) / Alam, Mustafa (Co-author) / Nguyen, Mong (Co-author) / Zhang, Zihan (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / WPC Graduate Programs (Contributor) / School of International Letters and Culture (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The purpose and goal of this project is to pinpoint a potential use case for Company X to invest in to sell their 5G modems. As 5G technology is growing to be a dominant force in global markets, Company X is looking to capitalize on the emerging technology by selling their 5G modems for Internet of Things applications. Research and gathering of information involved understanding cellular connectivity, modem operations and applications, companies in related industries, the history of the wireless spectrum, the pillars of 5G technology, and the plethora of use cases enabled by 5G. Looking at smart street lights as a potential use case for Company X, analyses were conducted to recommend whether Company X should invest in smart street lights. These analyses ranged from researching Company X’s competitors to performing a pro forma financial analysis to see if it is financially viable for Company X to enter the smart street light industry. The final recommendation is for Company X to not invest in smart street lighting.
ContributorsPannala, Ishan R (Co-author) / Alcaron, Sandra (Co-author) / Nilles, Robert (Co-author) / Wells, Dwight (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05