This project will be based in an objective and as well as subjective study related to Quality of Experience in a video conference application, appear.in. Indeed the study puts emphasis on finding possible correlations between Quality of Service parameters, that in the study are represented by some of the most meaningful network parameters, and contextual parameters with Quality of Experience. To achieve this the study was based in two different scenarios using two test environments. One is based on having an all free test and the other conducted in a created test-bad for monitoring and altering network parameters to see how user respond to the changes. Both scenarios were accompanied by a survey that provided useful information about the subjective perception of different individuals that took part in the testing phase.The project is divided in two phases where we set up the required environments for the test and then we process results to find correlation between the above mentioned parameters.In the end of the project all the results and the conclusions are presented providing a clearer view for what was done. The project itself might represent a good foundation to be used in the future for other studies related to video conferencing application.

For agencies in the health and human services field, data reporting is the primary source of information from which progress and organizational outcomes are measured. Often, front-line staff are responsible for collecting these data and yet are often ill-equipped to collect data accurately and consistently leading to a performance deficit affecting the strength and success of the program. Evaluating and addressing performance issues in the human services field has long been a challenge due to limited resources. Finding an effective and efficient method of evaluation that leads to a function-based intervention would improve performance and ultimately improve organizational outcomes. This study evaluated the Performance Diagnostic Checklist – Human Services (PDC-HS) as means of assessing the performance deficit of inaccurate data reporting in a community-based adult autism services program. A multiple-baseline design was used across four participants to evaluate the effects of the interventions indicated by the PDC-HS. Two participants received performance feedback and two received additional training following the behavior skills training model. Results indicated that both interventions were effective in increasing accurate data reporting across participants. Social validity measures also indicated the PDC-HS was easy to use and could be completed in a timely manner. / Applied Behavioral Analysis

We develop and analyze a class of CAC algorithms for resource management in wireless networks with the goal not only to satisfy QoS constraints, but also to maximize a value or reward objective function specified by the system. We demonstrate through analytical modeling and simulation validation that the CAC algorithms developed in this research for resource management can greatly improve the system reward obtainable with QoS guarantees, when compared with existing CAC algorithms designed for QoS satisfaction only. We design hybrid partitioning-threshold, spillover and elastic CAC algorithms based on the design techniques of partitioning, setting thresholds and probabilistic call acceptance to use channel resources for servicing distinct QoS classes. For each CAC algorithm developed, we identify optimal resource management policies in terms of partitioning or threshold settings to use channel resources. By comparing these CAC algorithms head-to-head under identical conditions, we determine the best algorithm to be used at runtime to maximize system reward with QoS guarantees for servicing multiple service classes in wireless networks. We study solution correctness, solution optimality and solution efficiency of the class of CAC algorithms developed. We ensure solution optimality by comparing optimal solutions achieved with those obtained by ideal CAC algorithms via exhaustive search. We study solution efficiency properties by performing complexity analyses and ensure solution correctness by simulation validation based on real human mobility data. Further, we analyze the tradeoff between solution optimality vs. solution efficiency and suggest the best CAC algorithm used to best tradeoff solution optimality for solution efficiency, or vice versa, to satisfy the system's solution requirements. Moreover, we develop design principles that remain applicable despite rapidly evolving wireless network technologies since they can be generalized to deal with management of 'resources' (e.g., wireless channel bandwidth), 'cells' (e.g., cellular networks), "connections" (e.g., service calls with QoS constraints), and "reward optimization" (e.g., revenue optimization in optimal pricing determination) for future wireless service networks. To apply the CAC algorithms developed, we propose an application framework consisting of three stages: workload characterization, call admission control, and application deployment. We demonstrate the applicability with the optimal pricing determination application and the intelligent switch routing application. / Ph. D.

Increasing size and prevalence of real time information have become important characteristics of databases found on the internet. Due to changing information, the relevancy ranking of the search results also changes. Current methods in information retrieval, which are based on offline indexing, are not efficient in such dynamic search spaces and cannot quickly provide the most current results. Due to the explosive growth of the internet, stove-piped approaches for dealing with dynamism by simply employing large computational resources are ultimately not scalable. A new processing methodology that incorporates intelligent resource allocation strategies is required. Also, modeling the dynamism in the search space in real time is essential for effective resource allocation. In order to support multi-grained dynamic resource allocation, we propose to use a partial processing approach that uses anytime algorithms to process the documents in multiple steps. At each successive step, a more accurate approximation of the final similarity values of the documents is produced. Resource allocation algorithm use these partial results to select documents for processing, decide on the number of processing steps and the computation time allocated for each step. We validate the processing paradigm by demonstrating its viability with image documents. We design an anytime image algorithm that uses a combination of wavelet transforms and machine learning techniques to map low level visual features to higher level concepts. Experimental validation is done by implementing the image algorithm within an established multiagent information retrieval framework called I-FGM. We also formulate a multiagent resource allocation framework for design and performance analysis of resource allocation with partial processing. A key aspect of the framework is modeling changes in the search space as external and internal dynamism using a grid-based search space model. The search space model divides the documents or candidates into groups based on its partial-value and portion processed. Hence the changes in the search space can be effectively represented in the search space model as flow of agents and candidates between the grids. Using comparative experimental studies and detailed statistical analysis we validate the search space model and demonstrate the effectiveness of the resource allocation framework. / Ph. D.

Visualization tools allow scientists to comprehend very large data sets and to discover relationships which are otherwise difficult to detect. Unfortunately, not all types of data can be visualized easily using existing tools. In particular, long sequences of nonnumeric data cannot be visualized adequately. Examples of this type of data include trace files of computer performance information, the nucleotides in a genetic sequence, a record of stocks traded over a period of years, and the sequence of words in this document. The term categorical time series is defined and used to describe this family of data. When visualizations designed for numerical time series are applied to categorical time series, the distortions which result from the arbitrary conversion of unordered categorical values to totally ordered numerical values can be profound. Examples of this phenomenon are presented and explained. Several new, general purpose techniques for visualizing categorical time series data have been developed as part of this work and have been incorporated into the Chitra perfor- mance analysis and visualization system. All of these new visualizations can be produced in O(n) time. The new visualizations for categorical time series provide general purpose techniques for visualizing aspects of categorical data which are commonly of interest. These include periodicity, stationarity, cross-correlation, autocorrelation, and the detection of recurring patterns. The effective use of these visualizations is demonstrated in a number of application domains, including performance analysis, World Wide Web traffic analysis, network routing simulations, document comparison, pattern detection, and the analysis of the performance of genetic algorithms. / Ph. D.

Supply chain management has been recently introduced to address the integration of organizational functions ranging from the ordering and receipt of raw materials throughout the manufacturing processes, to the distribution and delivery of products to the customer. Its application demonstrates that this idea enables organizations to achieve higher quality products, better customer service, and lower inventory cost. In order to achieve high performance, supply chain functions must operate in an integrated and coordinated manner. Several challenging problems associated with integrated supply chain design are: (1) how to model and coordinate the supply chain business processes, specifically in the area of supply chain workflows; (2) how to analyze the performance of an integrated supply chain network so that optimization techniques can be employed to improve customer service and reduce inventory cost; and (3) how to evaluate dynamic supply chain networks and obtain a comprehensive understanding of decision-making issues related to supply network configurations. These problems are most representative in the supply chain theory's research and applications. There are three major objectives for this research. The first objective is to develop viable modeling methodologies and analyzing algorithms for supply chain business processes so that the logic properties of supply chain process models can be analyzed and verified. This problem has not been studied in integrated supply chain literature to date. To facilitate the modeling and verification analysis of supply chain workflows, an object-oriented Petri nets based modular modeling and analyzing approach is presented. The proposed, structured, process-modeling algorithm provides an effective way to design structured supply chain business processes. The second objective is to develop a network of inventory-queue models for the performance analysis and optimization of an integrated supply network with inventory control at all sites. An inventory-queue is a queueing model that incorporates an inventory replenishment policy for the output store. This dissertation extends the previous work done on the supply network model with base-stock control and service requirements. Instead of one-for-one base stock policy, batch-ordering policy and lot-sizing problems are considered. To determine the replenishment lead times of items at the stores, a fixed-batch target-level production authorization mechanism is employed to explicitly obtain performance measures of the supply chain queueing model. The validity of the proposed model is illustrated by comparing the results from the analytical performance evaluation model and those obtained from the simulation study. The third objective is to develop simulation models for understanding decision-making issues of the supply chain network configuration in an integrated environment. Simulation studies investigate multi-echelon distribution systems with installation stock reorder policy and echelon stock reorder policy. The results show that, depending on the structure of multi-echelon distribution systems, either echelon stock or installation stock policy may be advantageous. This dissertation presents a new transshipment policy, called "alternate transshipment policy," to improve supply chain performance. In an integrated supply chain network that considers both the distribution function and the manufacturing function, the impacts of component commonality on network performance are also evaluated. The results of analysis-of-variance and Tukey's tests reveal that there is a significant difference in performance measures, such as delivery time and order fill rates, when comparing an integrated supply chain with higher component commonality to an integrated supply chain with lower component commonality. Several supply chain network examples are employed to substantiate the effectiveness of the proposed methodologies and algorithms. / Ph. D.

From flickering films to microlearning in 4K, the field known as Instructional Design and Technology (IDT) today has a long history, marked by constant evolution since the 1920s. The field has been thrust into the spotlight by the COVID-19 pandemic. This bibliometric analysis examines research articles from the Educational Technology Research and Development (ETRandD) journal spanning 1957 to 2023. The study addresses the challenges posed by the influx of new IDT professionals from diverse backgrounds, as well as the opportunity presented by the occasion of the centennial celebration of the Association for Educational Communications and Technology (AECT), the key professional organization in the field. Data for this study was primarily obtained from the Web of Science, supplemented by SCOPUS and Dimensions. The analysis utilized Biblioshiny to evaluate the journal's performance and impact, identifying influential works and authors. Additionally, VOSviewer was employed to map the scientific landscape, tracing the evolution of research themes and the collaborative networks among authors, institutions, and countries. While the analysis navigates through the limitations inherent in focusing on a single journal, majorly employing a specific dataset, and adhering to an English-only corpus, it successfully illuminates the core tendencies and transformative shifts within IDT. The findings emphasize the field's enduring commitment to leveraging technological advancements within a theoretically grounded framework to enhance learning outcomes. As the field continues to evolve in response to new entrants from diverse backgrounds, emerging technologies and shifting learners' contexts, this study provides a useful reference point for future research. / Doctor of Philosophy / The field of Instructional Design and Technology (IDT) has evolved dramatically from its early days in the 1920s, using simple film-based instruction, to today's sophisticated 4K microlearning environments. The field has been thrust into the limelight by the unprecedented challenges of the COVID-19 pandemic. This study is a bibliometric analysis of articles published in the Educational Technology Research and Development journal from 1957 to 2023. The research addresses the challenge of the entry of professionals from varied backgrounds into the IDT field and celebrates a century of contributions by the Association for Educational Communications and Technology (AECT), a leading organization in this discipline. Using databases like the Web of Science, SCOPUS, and Dimensions, and tools such as Biblioshiny and VOSviewer, the study maps the development of IDT research themes, identifies key works, and authors, and explores the collaborative networks that span authors, institutions, and countries. Despite focusing on a single journal and an English-only dataset, the analysis reveals significant trends and shifts in IDT. It underscores the field's commitment to integrating new technologies within a solid theoretical framework to improve educational outcomes. This study not only reflects on the past but also sets the stage for future research as IDT continues to adapt to the needs of diverse learners and emerging technologies.

A future Internet of Things (IoT) system will consist of a huge quantity of heterogeneous IoT devices, each capable of providing services upon request. It is of utmost importance for an IoT device to know if another IoT service is trustworthy when requesting it to provide a service. In this dissertation research, we develop trust-based service management techniques applicable to distributed, centralized, and hybrid IoT environments. For distributed IoT systems, we develop a trust protocol called Adaptive IoT Trust. The novelty lies in the use of distributed collaborating filtering to select trust feedback from owners of IoT nodes sharing similar social interests. We develop a novel adaptive filtering technique to adjust trust protocol parameters dynamically to minimize trust estimation bias and maximize application performance. Our adaptive IoT trust protocol is scalable to large IoT systems in terms of storage and computational costs. We perform a comparative analysis of our adaptive IoT trust protocol against contemporary IoT trust protocols to demonstrate the effectiveness of our adaptive IoT trust protocol. For centralized or hybrid cloud-based IoT systems, we propose the notion of Trust as a Service (TaaS), allowing an IoT device to query the service trustworthiness of another IoT device and also report its service experiences to the cloud. TaaS preserves the notion that trust is subjective despite the fact that trust computation is performed by the cloud. We use social similarity for filtering recommendations and dynamic weighted sum to combine self-observations and recommendations to minimize trust bias and convergence time against opportunistic service and false recommendation attacks. For large-scale IoT cloud systems, we develop a scalable trust management protocol called IoT-TaaS to realize TaaS. For hybrid IoT systems, we develop a new 3-layer hierarchical cloud structure for integrated mobility, service, and trust management. This architecture supports scalability, reconfigurability, fault tolerance, and resiliency against cloud node failure and network disconnection. We develop a trust protocol called IoT-HiTrust leveraging this 3-layer hierarchical structure to realize TaaS. We validate our trust-based IoT service management techniques developed with real-world IoT applications, including smart city air pollution detection, augmented map travel assistance, and travel planning, and demonstrate that our trust-based IoT service management techniques outperform contemporary non-trusted and trust-based IoT service management solutions. / Ph. D.

The use of aerial platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, has emerged as a promising solution for providing reliable and cost-effective wireless communications. In particular, UAVs can be quickly and efficiently deployed to support cellular networks and enhance their quality-of-service (QoS) by establishing line-of-sight communication links. With their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. Remarkably, despite these inherent advantages of UAVbased communications, little work has analyzed the performance tradeoffs associated with using UAVs as aerial wireless platforms. The key goal of this dissertation is to develop the analytical foundations for deployment, performance analysis, and optimization of UAV-enabled wireless networks. This dissertation makes a number of fundamental contributions to various areas of UAV communications that include: 1) Efficient deployment of UAVs, 2) Performance evaluation and optimization, and 3) Design of new flying, three-dimensional (3D) wireless systems. For deployment, using tools from optimization theory, holistic frameworks are developed for the optimal 3D placement of UAV base stations in uplink and downlink scenarios. The results show that the proposed deployment approaches significantly improve the downlink coverage for ground users, and enable ultra-reliable and energy-efficient uplink communications in Internet of Things (IoT) applications. For performance optimization, a novel framework is developed for maximizing the performance of a UAV-based wireless system, in terms of data service, under UAVs’ flight time constraints. To this end, using the mathematical framework of optimal transport theory, the optimal cell associations, that lead to a maximum data service to ground users within the limited UAVs’ hover duration, are analytically derived. The results shed light on the tradeoff between hover time and quality-of-service in UAV-based wireless networks. For performance evaluation, this dissertation provides a comprehensive analysis on the performance of a UAV-based communication system in coexistence with a terrestrial network. In particular, a tractable analytical framework is proposed for analyzing the coverage and rate performance of a network with a UAV base station and deviceto-device (D2D) users. The results reveal the fundamental tradeoffs in such a UAV-D2D network that allow adopting appropriate system design parameters. Then, this dissertation sheds light on the design of three new drone-enabled wireless systems. First, a novel framework for effective use of cache-enabled UAVs in wireless networks is developed. The results demonstrate how the users’ quality of experience substantially improves by exploiting UAVs’ mobility and user-centric information. Second, a new framework is proposed for deploying and operating a drone-based antenna array system that delivers wireless service to ground users within a minimum time. The results show significant enhancement in QoS, spectral and energy efficiency while levering the proposed drone antenna array system. Finally, to effectively incorporate various use cases of drones ranging from aerial users to base stations, the new concept of a fully-fledged 3D cellular network is introduced. For this new type of 3D wireless network, a unified framework for deployment, network planning, and performance optimization is developed that yields a maximum coverage and minimum latency in the network. In a nutshell, the analytical foundations and frameworks presented in this dissertation provide key guidelines for effective design and operation of UAV-based wireless communication systems. / Ph. D.

Currently, water utilities are facing monetary crises to maintain and expand services to meet the current as well as the future demands. Standard practice in pipeline infrastructure asset management is to collect data and predict the condition of pipelines using models and tools. Water utilities want to be proactive in fixing or replacing the pipes as fixing-when-it-fails ideology leads to increased cost and can affect environmental quality and societal health. There is a number of modeling techniques available for assessing the condition of the pipelines, but there is a massive shortage of methods to check the reliability of the results obtained using different modeling techniques. It is mainly because of the limited data one utility collects and absence of piloting of these models at various water utilities. In general, water utilities feel confident about their in-house condition prediction and failure models but are willing to utilize a reliable methodology which can overcome the issues related to the validation of the results. This paper presents the methodology that can enhance the reliability of model results for water pipeline performance analysis which can be used to parallel the output of the real system with confidence. The proposed methodology was checked using the dataset of two large water utilities and was found that it can potentially help water utilities gain confidence in their analyses results by statistically signifying the results. / Master of Science