MS, University of Cincinnati, 2022, Education, Criminal Justice, and Human Services: Information Technology

With families increasingly moving towards smart devices and home automation, the right security policies and access control are essential. However, in multi-person and family homes, several users are sharing an IoT device, bringing up the question of who's in control. We examine how smart-home owners share their IoT devices, and how they feel about using sharing features. In a global landscape, understanding cultural differences is key in every field, and IoT is no different. Using a mixture of survey and interview methods, we collect data regarding smart-home owners' IoT devices and which of these devices they share to understand their device sharing preferences. We then expand our findings by understanding the user demographics and cultural differences.

Committee: Nora McDonald Ph.D. (Committee Member); Jess Kropczynski Ph.D. (Committee Member) Subjects: Information Technology

Master of Sciences (Engineering), Case Western Reserve University, 2021, EECS - Computer and Information Sciences

Wearable Data has become prominent in the field of digital health due to its wide variety of data categories, high availability and ease of acquisition. A well-designed platform to recruit participants and collect their behavioral survey and wearable data should be easy to monitor and cost-efficient, as well as secure and HIPAA compliant. In this study, we developed a full-stack platform for data-driven digital health studies, the HeartBit Platform, consisting of a mobile application and a backend infrastructure. The platform implements Quality Control protocols to alert researchers of any issues, utilizes automated programs that can be conveniently supervised and employs state of the art security to ensure privacy and data security. We demonstrated the application of the HeartBit Platform on two human studies, in which we recruited 231 participants contributing to 10,158 behavioral surveys and 70,761 wearable data files of various categories to date.

Committee: Jing Li (Committee Chair); Xiao Li (Committee Co-Chair); Andy Podgurski (Committee Member) Subjects: Bioinformatics; Biomedical Research; Computer Science; Health; Health Care; Health Care Management; Health Education; Health Sciences

Doctor of Philosophy, Case Western Reserve University, 2020, EECS - Electrical Engineering

The pervasive Internet of Thing (IoT) revolution is driving the need for fundamental innovations in sensing, electronics, and ubiquitous computing that enable scalable, miniaturized, secure, and energy-efficient devices and networks. Due to some exceptional properties (such as small form factors, low phase noise thanks to ultra-high quality factor (Q), low power consumption, robustness to shock and vibration, amenability to monolithic integration with standard CMOS technology, compatibility with batch manufacturing, and wide operating temperature range), stable and self-sustained oscillators enabled by micro- and nano-electromechanical systems (MEMS and NEMS) devices have a myriad of applications including Internet of Things/Everything (IoTs/E) sensor nodes, next-generation wireless transceivers, RF signal processors, precision sensors, and navigation systems (e.g., GNSS). This dissertation focuses on ultra-stable MEMS-referenced oscillators for such applications. It describes the design, simulation, and experimental verification of three generations of digitally-programmable single-chip application-specific-integrated-circuits (ASICs) that can be integrated with MEMS and NEMS resonators to generate stable reference oscillators in the 10 kHz–16 MHz frequency range. We have also shown various functionalities of these amplifiers, including adaptive control of input impedance, automatic level control (ALC), automatic cancellation of parasitic electrical feedthrough in order to increase the signal to background ratio (SBR), optimizing the trade-off between tunability and stability, parametric pumping, frequency-locked loop (FLL) to stabilize the output frequency, and phase-controlled-closed-loop (PCCL) operation to find the optimal operating point. The chips have been used to realize i) 7 MHz ultra-stable (-105 dBc/Hz @ Δf = 10 Hz) single and quadrature oscillators based on an ultra-high-Q ( 3.2 × 10^6 at Vp = 35V) wafer-level vacuum-encapsulated single-cryst (open full item for complete abstract)

Committee: Soumyajit Mandal PhD (Committee Chair); Christian Zorman PhD (Committee Member); Hossein Lavasani PhD (Committee Member); Xiong (Bill) Yu PhD (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Ohio University, 2018, Computer Education and Technology (Education)

The rapid advancement in information technology and the ubiquitous penetration of the Internet are heralding an experience in the world where every physical device is interconnect-able to other devices and the Internet. IoT forms the core of this new wave of ubiquitous technologies. This nascent technology is opening new and virtually inexhaustible sources of innovation in various sectors. As the education sector transitions to technologically augmented learning, IoT offers a great potential in the realm of higher education where some principles of IoT are already in use. The purpose of this study was to explore how IoT can enhance data-driven decision- making (D3M) in the teaching and learning process in higher education. Six faculty, seven students, and four administrators participated in this study. A qualitative case study was used to explore how IoT can be used to enhance D3M. Individual interviews and document analysis approach was used in data collection. Multiple techniques facilitated the data analysis. Provision coding was applied in the first cycle coding to organize the data into categories. Pattern coding was implemented in the second cycle coding to condense code summaries from first cycle coding into precise themes. Unified framework of construct validity was applied to enhance the credibility and dependability of the study. Findings revealed that participants engaged with IoT to enhance the learning experience, improve collaboration on projects, augment student-centered teaching, support customized teaching, and learning, facilitate seamless learning, and parity for diverse learners. Participants had mixed concerns about the issue of individual privacy, data security and connectivity challenges. The constructs of UTAUT2 framework was used to explore the beliefs and perceptions that influence the adoption of IoT amongst the participants in higher education. The conclusion drawn from this study elucidated that if correctly implemented, IoT (open full item for complete abstract)

Committee: Greg Kessler (Advisor) Subjects: Educational Software; Educational Technology; Information Systems; Instructional Design