With the rapid growth of Internet technologies as well as the explosion of connected objects, Internet of Things (IoT) is considered an Internet revolution that positively affects several life aspects. The integration of IoT solutions and cloud computing, namely cloud-based IoT, is a crucial concept to meet these demands. However, two major challenges of the cloud-based IoT are interoperability and reliability.
In this thesis, our main objective is to deal with the interoperability and reliability issues that arise from large-scale deployment. The proposed solutions spread over architectures, models, and algorithms, ultimately covering most of the layers of the IoT architecture. At the communication layer, we introduce a method to interoperate heterogeneous IoT connections by using a connector concept. We then propose an error and change point detection algorithm powered by active learning to enhance IoT data reliability. To maximize usable knowledge and business value from this cleaned data and make it more interoperable, we introduce a virtual sensor framework that simplifies creating and configuring virtual sensors with programmable operators. Furthermore, we provide a novel descriptive language, which semantically describes groups of Things. To ensure the device reliability, we propose an algorithm that minimizes energy consumption by real-time estimating the optimal data collection frequency. The efficiency of our proposals has been practically demonstrated in a cloud-based IoT platform of a start-up company
