Recent technological developments have led to significant changes in the power grid. Increasing consumption, widespread adoption of Distributed Energy Resources (DER), installation of smart meters, these are some of the many factors that characterize the changing distribution network. These transformations taking place at the edge of the grid call for improved planning and operation practices. In this context, this thesis aims to improve the grid edge functionality by putting forth a method to address the problem of high demand during peak period by identifying customer groups for participation in demand response programs, which can lead to significant peak shaving for the utility. A possible demand response strategy for peak shaving makes use of Photovoltaic (PV) and Battery energy storage system (BESS). In the process, this work also examines the approach to computation of hosting capacity (HC) for small PV and quantifies the difference obtained in HC when a detailed Low voltage (LV) network is available and included in HC studies. Most PV hosting studies assess the impact on system feeders with aggregated LV loads. However, as more residential customers adopt rooftop solar, the need to include secondary network models in the analysis is studied by performing a comparative study of hosting capacity for a feeder with varying loading information available. / Master of Science / Today, with significant technological advancements, as we proceed towards a modern grid, a mere change in physical infrastructure will not be enough. With the changes in kinds of equipment installed on the grid, a wave of transformation has also begun to flow in the planning and operation practices for a smarter grid. Today, the edge of the grid where the customer is interfaced to the power system has become extremely complex. Customers can use rooftop solar PV to generate their own electricity, they are more informed about their consumption behavior due to installation of smart meters and also have options to integrate other technology like battery energy storage system and electric vehicles. Like with any good technology, adoption of these advancements in the system brings with itself a greater need for reform in operation and planning of the system. For instance, increasing installation of rooftop solar at the customer end calls for review of existing methods that determine the maximum level of PV deployment possible in the network without violating the operating conditions. So, in this work, a comparative study is done to review the PV hosting capacity of a network with varying levels of information available. And the importance of utilities to have secondary network models available is emphasized. With PV deployed in the system, enhanced demand response strategies can be formulated by utilities to tackle high demand during peak period. In a bid to identify customers for participation in such programs, in this work, a computationally efficient strategy is developed to identify customers with high demand during peak period, who can be incentivized to participate in demand response programs. With this, a significant peak shaving can be achieved by the utility, and in turn stress on the distribution network is reduced during peak hours.

<p dir="ltr">Blockchain technology, once the backbone of Bitcoin, has burgeoned into a powerhouse of potential, signaling a revolutionary shift across various sectors, including finance, supply chains, and digital identity. This paradigm shift, which replaces trust in centralized entities with a decentralized ledger of transparency, is rapidly gaining traction among global entities. Despite the promise, blockchain's smart contract evolution has also introduced significant risks, as demonstrated by notorious breaches like the DAO hack. This research offers a dual-focused inquiry into the technological sophistication and social implications of blockchain, particularly smart contracts, assessing both their promise and their perils. It meticulously examines their design, potential vulnerabilities, and recounts sobering lessons from historical breaches.</p><p dir="ltr">To address these concerns, the study presents advanced strategies for vulnerability detection and proactive remedies, recognizing the critical need for security in our digitally convergent economy. In Chapter 3, a novel methodology is employed that uses a comprehensive dataset against advanced detection tools, aiming to address and mitigate vulnerabilities. Chapter 4 provides empirical evidence of the methodology's efficacy, underpinning a critical discussion with real-world applicability and challenges.</p><p dir="ltr">Ultimately, this paper acts as a clarion call for vigilant and innovative strides in blockchain security, emphasizing the technology's vast capabilities against the need for solidified trust. It invites the global research community to join a collaborative effort in addressing the open challenges and fostering advancements to ensure the safe expansion of blockchain technology.</p>

The development of an Indoor Positioning System that requires a non-invasive setup and installation process is outlined in this dissertation. The Hardware, Mechanical and Software components are described in complete detail. The system operates using a hybrid of Bluetooth Low Energy (BLE) signal strength analysis and proximity sensor data collection to determine the location of a known Bluetooth compatible device. Additionally, a dynamic remote calibration protocol was developed to ensure a safe and smooth setup and integration process in any location the system is implemented. The system uses custom designed beacon modules that connect directly to outlets in designated rooms. These beacons relay sensor and BLE data to a Hub module that collects and stores all this data locally and on a cloud server. These features ensured that the IPS is a completely remote device that can be setup independently by the user. To our knowledge, this is the only Indoor Positioning System that does not require prior knowledge of the location of integration and the need for an in-person setup and calibration process. Additionally, despite the lack of an extensive setup and calibration process the system still operates at an accurate room detection percentage of 98%. To further prove its ease of use the system has been implemented in a clinical study where several older adults (65+) have integrated this system within their homes. This system has been designed to act as the foundation for larger scale healthcare monitoring applications. / Thesis / Candidate in Philosophy / Indoor positioning technology acts as the foundation for several healthcare monitoring networks. An accurate and easy to use indoor positioning system will entail how effective the overall healthcare monitoring platform is. Additionally, indoor positioning itself can be accomplished in several different ways. Some of these approaches include the use of physical sensors to detect presence, signal strength approximations via some sort of communication protocol or even the use of secure entry via RFID identification tags. Currently, most of the systems that use one of these approaches require extensive setup and calibration processes and extensive knowledge of the tracking locations. However, this is not always practical especially when the system is integrated in a large-scale environment like a retirement home. A system with an easy- to-use setup and installation platform is needed to complete these high impact healthcare monitoring projects.

The population of the world is increasing exponentially each year with a large population base. Agricultural fields are facing the pressure of dealing with food insufficiency, whereas the challenges of limited resources of arable land and fresh water on the earth should be taken into account at the same time. Smart farming was born at the right time to cope with the problem and has become one of the most powerful approaches to reducing the ecological footprint of farming and improving agricultural yield. The four most important variables that impact crop yield are soil productivity, the accessibility of water, climate, and pests or diseases. This thesis emphasizes the application of chemical fertilizers to corn and disregards the impact of water, pests, and disease for the moment. In this study, three scenarios are explored deeper one by one. The only factor that varies among the three scenarios is the nitrogen amount available to the plant. Fertilizers have outstanding performance in improving the yield and quality of plants in agricultural fields, and this is the emphasis of this thesis. Compared with the fertilizer properties and characteristics of frequently used commercial fertilizers, polymer-coated urea was selected as the fertilizer in this study because the feature of nitrogen can be released into the soil slowly and in a controlled manner. Scenario 1 created an ideal condition where unlimited nitrogen was provided to the corn. Scenario 2 assumed that a fixed amount of polymer-coated urea was applied at the beginning of the sowing season only. Scenario 3 figured out an optimal yield by separating the fertilizer application at the beginning and in the middle of the growing days with the same amounts of fertilizer used in Scenario 2. The model was performed based on historical data from Oklahoma and Ottawa using IoT sensors. The simulation model generated with Python figured out that approximately the end of June to the start of July is the best time to apply the remaining fertilizer, assuming that the sowing stage starts on May 1. The percentage of polymer-coated urea applied initially was found to usually be around 10% in the tested regions. The model was used to predict the yield in Ottawa using from 40.94 g/(m^2) in Scenario 2 to 55.43 g/(m^2) in Scenario 3, achieving an outstanding increasing rate of 35.38%.

The world’s population is expected to grow to 9.6 billion by 2050. This exponential growth imposes a significant challenge on food security making the development of efficient crop production a growing concern. The traditional methods of analyzing soil and crop yield rely on manual field surveys and the use of expensive instruments. This process is not only time-consuming but also requires a team of specialists making this method of prediction expensive. Prediction of yield is an integral part of smart farming as it enables farmers to make timely informed decisions and maximize productivity while minimizing waste. Traditional statistical approaches fall short in optimizing yield prediction due to the multitude of diverse variables that influence crop production. Additionally, the interactions between these variables are non-linear which these methods fail to capture. Recent approaches in machine learning and data-driven models are better suited for handling the complexity and variability of crop yield prediction. Maize, also known as corn, is a staple crop in many countries and is used in a variety of food products, including bread, cereal, and animal feed. In 2021-2022, the total production of corn was around 1.2 billion tonnes superseding that of wheat or rice, making it an essential element of food production. With the advent of remote sensing, Unmanned aerial vehicles or UAVs are widely used to capture high-quality field images making it possible to capture minute details for better analysis of the crops. By combining spatial features, such as topography and soil type, with crop growth information, it is possible to develop a robust and accurate system for predicting crop yield. Convolutional Neural Networks (CNNs) are a type of deep neural network that has shown remarkable success in computer vision tasks, achieving state-of-the-art performance. Their ability to automatically extract features and patterns from data sets makes them highly effective in analyzing complex and high-dimensional datasets, such as drone imagery. In this research, we aim to build an effective crop yield predictor using data fusion and deep learning. We propose several Deep CNN architectures that can accurately predict corn yield before the end of the harvesting season which can aid farmers by providing them with valuable information about potential harvest outcomes, enabling them to make informed decisions regarding resource allocation. UAVs equipped with RGB (Red Green Blue) and multi-spectral cameras were scheduled to capture high-resolution images for the entire growth period of 2021 of 3 fields located in Ottawa, Ontario, where primarily corn was grown. Whereas, the ground yield data was acquired at the time of harvesting using a yield monitoring device mounted on the harvester. Several data processing techniques were employed to remove erroneous measurements and the processed data was fed to different CNN architectures, and several analyses were done on the models to highlight the best techniques/methods that lead to the most optimal performance. The final best-performing model was a 3-dimensional CNN model that can predict yield utilizing the images from the Early(June) and Mid(July) growing stages with a Mean Absolute Percentage error of 15.18% and a Root Mean Squared Error of 17.63 (Bushels Per Acre). The model trained on data from Field 1 demonstrated an average Correlation Coefficient of 0.57 between the True and Predicted yield values from Field 2 and Field 3. This research provides a direction for developing an end-to-end yield prediction model. Additionally, by leveraging the results from the experiments presented in this research, image acquisition, and computation costs can be brought down.

Indiana University-Purdue University Indianapolis (IUPUI) / Data and information have increased exponentially in recent years. The promising era of big data is advancing many new practices. One of the emerging big data applications is healthcare. Large quantities of data with varying complexities have been leading to a great need in smart and secure big data systems. Mobile edge, more specifically the smart phone, is a natural source of big data and is ubiquitous in our daily lives. Smartphones offer a variety of sensors, which make them a very valuable source of data that can be used for analysis. Since this data is coming directly from personal phones, that means the generated data is sensitive and must be handled in a smart and secure way. In addition to generating data, it is also important to interact with the big data. Therefore, it is critical to create edge systems that enable users to access their data and ensure that these applications are smart and secure. As the first major contribution of this thesis, we have implemented a mobile edge system, called s2Edge. This edge system leverages Amazon Web Service (AWS) security features and is backed by an AWS cloud system. The implemented mobile application securely logs in, signs up, and signs out users, as well as connects users to the vast amounts of data they generate. With a high interactive capability, the system allows users (like patients) to retrieve and view their data and records, as well as communicate with the cloud users (like physicians). The resulting mobile edge system is promising and is expected to demonstrate the potential of smart and secure big data interaction. The smart and secure transmission and management of the big data on the cloud is essential for healthcare big data, including both patient information and patient measurements. The second major contribution of this thesis is to demonstrate a novel big data cloud system, s2Cloud, which can help enhance healthcare systems to better monitor patients and give doctors critical insights into their patients' health. s2Cloud achieves big data security through secure sign up and log in for the doctors, as well as data transmission protection. The system allows the doctors to manage both patients and their records effectively. The doctors can add and edit the patient and record information through the interactive website. Furthermore, the system supports both real-time and historical modes for big data management. Therefore, the patient measurement information can, not only be visualized and demonstrated in real-time, but also be retrieved for further analysis. The smart website also allows doctors and patients to interact with each other effectively through instantaneous chat. Overall, the proposed s2Cloud system, empowered by smart secure design innovations, has demonstrated the feasibility and potential for healthcare big data applications. This study will further broadly benefit and advance other smart home and world big data applications. / 2023-06-01

While progress has been made in querying digital information contained in XML and HTML documents, success in retrieving information from the so called "hidden Web" (data behind Web forms) has been modest. There has been a nascent trend of developing autonomous tools for extracting information from the hidden Web. Automatic tools for ontology generation, wrapper generation, Weborm querying, response gathering, etc., have been reported in recent research. This thesis presents a system called Chameleon for automatic querying of and response gathering from the hidden Web. The approach to response gathering is based on automatic table structure identification, since most information repositories of the hidden Web are structured databases, and so the information returned in response to a query will have regularities. Information extraction from the identified record structures is performed based on domain knowledge corresponding to the domain specified in a query. So called "domain plug-ins" are used to make the dynamically generated wrappers domain-specific, rather than conventionally used document-specific.

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