A model-based approach for data processing in IoT environments

The recent advances in several areas, including sensor technologies, networking, and data processing, have enabled the Internet of Things (IoT) vision to become more and more a reality every day. As a consequence of these advances, the IoT of today allows the development of sophisticated applications for IoT environments, such as smart cities, smart homes, or smart factories. Due to continuous sensor measurements and frequent data exchange among so-called IoT objects, the data generated within an IoT environment incorporate the form of data streams. With this increasing amount of data to be continuously processed, several challenges arise while aiming at an efficient processing of IoT data. For instance, how IoT data processing can be realized, so that meaningful information can be derived without affecting the reactiveness of IoT applications. Furthermore, how different functional, non-functional, and user-defined requirements of IoT applications can be satisfied by the IoT data processing. In this PhD thesis, a new holistic approach for processing data stream-based applications within IoT environments is presented. Its focus lies on efficient placement of operators of data stream applications onto heterogeneous, distributed, dynamic IoT environments. In contrast to state-of-the-art operator placement, this approach takes into consideration additional requirements introduced by the peculiar characteristics of the Internet of Things. Furthermore, non-functional and user-defined requirements are also taken into consideration. This PhD thesis is supported by different informational models and operator placement techniques, so that the entire life cycle of IoT environments and data stream-based applications can be easily managed. IoT environments and their processing capabilities are described by IoT environment models (IoTEM). Likewise, the business logic of IoT applications and their requirements are defined by data stream processing models (DSPM). Based on these informational models, several algorithms determine feasible placements of processing operators onto IoT objects of IoT environments, so that the aforementioned requirements and capabilities are matched. In this approach, one of the main goals is to process IoT data as near to data sources as possible, so that cloud infrastructures are employed only in cases where IoT environments do not offer sufficient processing resources for the IoT application. The execution of data processing on both IoT environments and cloud infrastructures is commonly known as fog computing. Through the approach of this PhD thesis, data processing of IoT applications can be tailored to particular use cases, supporting the specific requirements of the domains, and furthermore, of IoT application users. Once feasible placements are determined, processing operators are then deployed onto corresponding IoT objects using standards, such as TOSCA, and the IoT application is considered up and running. Finally, the IoT environment is continuously monitored in order to recognize and react to disturbances affecting the data processing of deployed IoT applications. The approach of this PhD thesis is supported by the Multi-purpose Binding and Provisioning Platform (MBP), an open-source IoT platform, which has been developed as a proof-of-concept of the contributions of this PhD thesis