A Model-Driven Framework for Enabling Flexible and Robust Mobile Data Collection Applications

In the light of the ubiquitous digital transformation, smart mobile technology has become a salient factor for enabling large-scale data collection scenarios. Structured instruments (e.g., questionnaires) are frequently used to collect data in various application domains, like healthcare, psychology, and social sciences. In current practice, instruments are usually distributed and filled out in a paper-based fashion (e.g., paper-and-pencil questionnaires). The widespread use of smart mobile devices, like smartphones or tablets, offers promising perspectives for the controlled collection of accurate data in high quality. The design, implementation and deployment of mobile data collection applications, however, is a challenging endeavor. First, various mobile operating systems need to be properly supported, taking their short release cycles into account. Second, domain-specific peculiarities need to be flexibly aligned with mobile application development. Third, domain-specific usability guidelines need to be obeyed. Altogether, these challenges turn both programming and maintaining of mobile data collection applications into a costly, time-consuming, and error-prone endeavor. The Ph.D. thesis at hand presents an advanced framework that shall enable domain experts to transform paper-based instruments to mobile data collection applications. The latter, in turn, can then be deployed to and executed on heterogeneous smart mobile devices. In particular, the framework shall empower domain experts (i.e., end-users) to flexibly design and create robust mobile data collection applications on their own; i.e., without need to involve IT experts or mobile application developers. As major benefit, the framework enables the development of sophisticated mobile data collection applications by orders of magnitude faster compared to current approaches, and relieves domain experts from manual tasks like, for example, digitizing and analyzing the collected data

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Enabling Sophisticated Lifecycle Support for Mobile Healthcare Data Collection Applications

The widespread dissemination of smart mobile devices enables new ways of collecting longitudinal data sets in a multitude of healthcare scenarios. On the one hand, mobile data collection can be accomplished more effectively and quicker compared with validated paper-based instruments. On the other hand, it can increase the data quality significantly and enable data collection in scenarios not covered by existing approaches so far. Previous attempts to utilize smart mobile devices for collecting data in these scenarios, however, often struggle with high costs for developing and maintaining mobile applications, which need to run on a multitude of mobile operating systems. Therefore, in the QuestionSys project, we are developing a generic (i.e., platform-independent) framework for enabling mobile data collection and sensor data integration in healthcare scenarios. The latter, in turn, is addressed by a model-driven approach, which is shown this paper along with the core components of the QuestionSys framework. In particular, it is shown how healthcare experts are empowered to create mobile data collection and sensing applications on their own and with reasonable efforts

Modular product development for mass customization

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Mass Customization of Housing: A Framework for Harmonizing Individual Needs with Factory Produced Housing

Integrated processes for design and fabrication have guided mass customization of architectural systems and components. Providing affordable and accessible housing, a vital segment of the building industry, is a multifaceted process that witnessed various manifestations towards individualization over the past few decades. Design flexibility in housing systems is becoming a crucial aspect, informed by consumers’ lifestyles, demographic patterns, and lifecycles change at a rapid pace. As the housing market demands more personalized, efficient, and agile strategies, prefabricated building systems have always presented a viable alternative for flexibility and cus-tomization, following a rise of interest in the last decade focused on new modes of digitized design and production. This paper presents an overview and appraisal of various practices to implement customization in the housing industry, with specific focus on empowering a systemic approach. We then propose an open framework that could accommodate emergent design technologies and production protocols, with the aim of taking advantage of advanced research efforts, and coupled with current industry application

Towards Flexible Mobile Data Collection in Healthcare

The widespread dissemination of smart mobile devices offers promising perspectives for a variety of healthcare data collection scenarios. Usually, the implementation of mobile healthcare applications for collecting patient data is cumbersome and time-consuming due to scenario-specific requirements as well as continuous adaptations to already existing mobile applications. Emerging approaches, therefore, aim to empower domain experts to create mobile data collection applications themselves. This paper discusses flexibility issues considered by a generic and sophisticated framework for realizing mobile data collection applications. Thereby, flexibility is discussed along different phases of data collection scenarios. Altogether, the realized flexibility significantly increases the practical benefit of smart mobile devices in healthcare data collection scenarios

The Impact of Information Technology on Mass Customization

Increasingly, companies are adopting mass customization as a business strategy to meet increasing customer demand for product variety to help compete in the global marketplace. This paper examines the effects and impacts of Information Technology (LT) on mass customization and how various IT tools are used as customizing mediums to enable mass customization and provide unique value to customers in the most efficient way possible. Examples are drawn from companies who leveraged IT technologies in their mass customization efforts. Recognizing the critical value of technology professionals, this paper further discusses IT professionals\u27 roles and how they are an integral part of the mass customization decision process. Additionally, some of the challenges that companies face when attempting to adopt Information Technology as an instrument to enable mass customization are examined. In this process, some possible solutions to these challenges are explored. Finally, some of the benefits and outcome of mass customization are discussed