Wireless Sensor Technology Selection for I4.0 Manufacturing Systems

The term smart manufacturing has surfaced as an industrial revolution in Germany known as Industry 4.0 (I4.0); this revolution aims to help the manufacturers adapt to turbulent market trends. Its main scope is implementing machine communication, both vertically and horizontally across the manufacturing hierarchy through Internet of things (IoT), technologies and servitization concepts. The main objective of this research is to help manufacturers manage the high levels of variety and the extreme turbulence of market trends through developing a selection tool that utilizes Analytic Hierarchy Process (AHP) techniques to recommend a suitable industrial wireless sensor network (IWSN) technology that fits their manufacturing requirements.In this thesis, IWSN technologies and their properties were identified, analyzed and compared to identify their potential suitability for different industrial manufacturing system application areas. The study included the identification and analysis of different industrial system types, their application areas, scenarios and respective communication requirements. The developed tool’s sensitivity is also tested to recommend different IWSN technology options with changing influential factors. Also, a prioritizing protocol is introduced in the case where more than one IWSN technology options are recommended by the AHP tool.A real industrial case study with the collaboration of SPM Automation Inc. is presented, where the industrial systems’ class, communication traffic types, and communication requirements were analyzed to recommend a suitable IWSN technology that fits their requirements and assists their shift towards I4.0 through utilizing AHP techniques. The results of this research will serve as a step forward, in the transformation process of manufacturing towards a more digitalized and better connected cyber-physical systems; thus, enhancing manufacturing attributes such as flexibility, reconfigurability, scalability and easing the shift towards implementing I4.0

Designing with and for Youth: A Participatory Design Research Approach for Critical Machine Learning Education

As big data algorithm usage becomes more ubiquitous, it will become critical for all young people, particularly those from historically marginalized populations, to have a deep understanding of data science that empowers them to enact change in their local communities and globally. In this study, we explore the concept of critical machine learning: integrating machine learning knowledge content with social, ethical, and political effects of algorithms. We modified an intergenerational participatory design approach known as cooperative inquiry to co-design a critical machine learning educational program with and for youth ages 9 - 13 in two after-school centers in the southern United States. Analyzing data from cognitive interviews, observations, and learner artifacts, we describe the roles of children and researchers as meta-design partners. Our findings suggest that cooperative inquiry and meta-design are suitable frameworks for designing critical machine learning educational environments that reflect children’s interests and values. This approach may increase youth engagement around the social, ethical, and political implications of large-scale machine learning algorithm deployment

Towards Real-World Federated Learning: Empirical Studies in the Domain of Embedded Systems

Context: Artificial intelligence (AI) has led a new phase of technical revolution and industrial development around the world since the twenty-first century, revolutionizing the way of production. Artificial intelligence (AI), an emerging information technology, is thriving, and AI application technologies are gaining traction, particularly in professional services such as healthcare, education, finance, security, etc. More machine learning technologies have begun to be thoroughly applied to the production stage as big data and cloud computing capabilities have improved. With the increased focus on Machine Learning applications and the rapid growth of distributed edge devices in the industry, we believe that utilizing a large number of edge devices will become increasingly important. The introduction of Federated Learning changes the situation in which data must be centrally uploaded to the cloud for processing and maximizes the use of edge devices\u27 computing and storage capabilities. With local data processing, the learning approach eliminates the need to upload large amounts of local data and reduces data transfer latency. Because Federated Learning does not require centralized data for model training, it is better suited to edge learning scenarios with limited data and privacy concerns. Objective: The purpose of this research is to identify the characteristics and problems of the Federated Learning methods, our new algorithms and frameworks that can assist companies in making the transition to Federated Learning, and empirically validate the proposed approaches. Method: To achieve these objectives, we adopted an empirical research approach with design science being our primary research method. We conducted a literature review, case studies, including semi-structured interviews and simulation experiments in close collaboration with software-intensive companies in the embedded systems domain. Results: We present four major findings in this paper. First, we present a state-of-the-art review of the empirical results reported in the existing Federated Learning literature. We then categorize those Federated Learning implementations into different application domains, identify their challenges, and propose six open research questions based on the problems identified in the literature. Second, we conduct a case study to explain why companies anticipate Federated Learning as a potential solution to the challenges they encountered when implementing machine learning components. We summarize the services that a comprehensive Federated Learning system must enable in industrial settings. Furthermore, we identify the primary barriers that companies must overcome in order to embrace and transition to Federated Learning. Based on our empirical findings, we propose five requirements for companies implementing reliable Federated Learning systems. Third, we develop and evaluate four architecture alternatives for a Federated Learning system, including centralized, hierarchical, regional, and decentralized architectures. We investigate the trade-o between communication latency, model evolution time, and model classification performance, which is critical for applying our findings to real-world industrial systems. Fourth, we introduce techniques and asynchronous frameworks for end-to-end on-device Federated Learning. The method is validated using a steering wheel angle prediction case. The local models of each edge vehicle can be continuously trained and shared with other vehicles to improve their local model prediction accuracy. Furthermore, we combine the asynchronous Federated Learning approach with Deep Neural Decision Forests and validate our method using important industry use cases in the automotive domain. Our findings show that Federated Learning can improve model training speed while lowering communication overhead without sacrificing accuracy, demonstrating that this technique has significant benefits to a wide range of real-world embedded systems. Future Work: In the future, we plan to test our approach in other use cases and look into more sophisticated neural networks integrated with our approach. In order to improve model training performance on resource-constrained edge devices in real-world embedded systems, we intend to design more appropriate aggregation methods and protocols. Furthermore, we intend to use the Federated Learning and Reinforcement Learning methods to assist the edge in evolving themselves autonomously and fully utilizing the computation capabilities of the edge devices

All in the Family: Exploring Design Personas of Systems for Remote Communication with Preschoolers

Although there have been recent advances in remote communication technologies that foster connectedness and intimacy over a distance, systems designed for communicating with preliterate preschoolers—a desired use case—are not yet prevalent, nor are there clear guidelines for their design. We conducted a mixed-methods study to characterize the current practices, goals, and needs of people who wish to use remote communication systems with young children. We present quantitative and qualitative findings on the motivations for communicating, the habits, activities, and patterns that have been established, and the barriers and concerns faced. We synthesized these findings into four design personas that describe the desired functionality and requirements of systems to support remote communication with preschoolers. For each persona, we systematically evaluated 60 research-based systems based on the extent to which each persona’s requirements were covered, demonstrating that none of the personas were greatly satisfied with the available tools

Developing sustainable business models for institutions’ provision of open educational resources: Learning from OpenLearn users’ motivations and experiences

Universities across the globe have, for some time, been exploring the possibilities for achieving public benefit and generating business and visibility through releasing and sharing open educational resources (OER). Many have written about the need to develop sustainable and profitable business models around the production and release of OER. Downes (2006), for example, has questioned the financial sustainability of OER production at scale. Many of the proposed business models focus on OER’s value in generating revenue and detractors of OER have questioned whether they are in competition with formal education. This paper reports on a study intended to broaden the conversation about OER business models to consider the motivations and experiences of OER users as the basis for making a better informed decision about whether OER and formal learning are competitive or complementary with each other. The study focused on OpenLearn - the Open University’s (OU) web-based platform for OER, which hosts hundreds of online courses and videos and is accessed by over 3,000,000 users a year. A large scale survey and follow-up interviews with OpenLearn users worldwide revealed that university provided OER can offer learners a bridge to formal education, allowing them to try out a subject before registering on a formal course and to build confidence in their abilities as learners. In addition, it was found that using OER during formal paid-for study can improve learners’ performance and self-reliance, leading to increased retention and satisfaction with the learning experience

Design and Implementation of a Portable Framework for Application Decomposition and Deployment in Edge-Cloud Systems

The emergence of cyber-physical systems has brought about a significant increase in complexity and heterogeneity in the infrastructure on which these systems are deployed. One particular example of this complexity is the interplay between cloud, fog, and edge computing. However, the complexity of these systems can pose challenges when it comes to implementing self-organizing mechanisms, which are often designed to work on flat networks. Therefore, it is essential to separate the application logic from the specific deployment aspects to promote reusability and flexibility in infrastructure exploitation. To address this issue, a novel approach called "pulverization" has been proposed. This approach involves breaking down the system into smaller computational units, which can then be deployed on the available infrastructure. In this thesis, the design and implementation of a portable framework that enables the "pulverization" of cyber-physical systems are presented. The main objective of the framework is to pave the way for the deployment of cyber-physical systems in the edge-cloud continuum by reducing the complexity of the infrastructure and exploit opportunistically the heterogeneous resources available on it. Different scenarios are presented to highlight the effectiveness of the framework in different heterogeneous infrastructures and devices. Current limitations and future work are examined to identify improvement areas for the framework

Open educational resources for all? Comparing user motivations and characteristics across The Open University’s iTunes U channel and OpenLearn platform.

With the rise in access to mobile multimedia devices, educational institutions have exploited the iTunes U platform as an additional channel to provide free educational resources with the aim of profile-raising and breaking down barriers to education. For those prepared to invest in content preparation, it is possible to produce interactive, portable material that can be made available globally. Commentators have questioned both the financial implications for platform-specific content production, and the availability of devices for learners to access it (Osborne, 2012). The Open University (OU) makes its free educational resources available on iTunes U and via its web-based open educational resources (OER) platform, OpenLearn. The OU’s OER on iTunes U reached the 60 million download mark in 2013; its OpenLearn platform boasts 27 million unique visitors since 2006. This paper reports the results of a large-scale study of users of the OU’s iTunes U channel and OpenLearn platform. A survey of several thousand users revealed key differences in demographics between those accessing OER via the web and via iTunes U. In addition, the data allowed comparison between three groups: formal learners, informal learners and educators. The study raises questions about whether university-provided OER meet the needs of users and makes recommendations for how content can be modified to suit their needs. As the publishing of OER becomes core to business, we reflect on reasons why understanding users’ motivations and demographics is vital, allowing for needs-led resource provision and content that is adapted to best achieve learner satisfaction, and to deliver institutions’ social mission