Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

With the introduction of the Internet of Things (IoT) into every area of life, more and more applications are created that rely on information collected from IoT sensors. These applications are developed by third-party developers and depend on a continuous information flow but do not operate the sensor network themselves. However, in case of sensor failures, the flow of information will be interrupted which will pose a problem for the user/application as well as the data provider who might not be able to replace the sensor in time. Depending on the requirements of the application, estimators which are trained through machine learning algorithms, called Virtual Sensors, can ensure the uninterrupted operation of the application. However, for a solution that can be used in practice, the deployment of such a Virtual Sensor needs to be fast enough as well as fully automated, so that no human interaction and domain knowledge is required. This paper describes a framework that is capable of finding correlating IoT sensors in the surrounding environment, selecting a machine learning algorithm, and training a fitting model to run a Virtual Sensor.

This paper deals with the use of Quality of Information (QoI) in the context of frameworks for data integration in the Internet of Things (IoT). It analyses existing approaches available to generate measures for information quality and proposes a concept to integrate real-time QoI calculation into an IoT framework. The approach shows several advantages when it comes to fault-detection, or the ranking of data streams when searching for IoT devices. However, the use of QoI often suffers from missing or incomplete metadata. This is addressed by a knowledge-based approach.