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IoT devices influence many different spheres of society and are predicted to have a huge impact on our future. Extracting real-time insights from diverse sensor data and dealing with the underlying uncertainty of sensor data are two main challenges of the IoT ecosystem In this paper, we propose a data processing architecture, M-DB, to effectively integrate and continuously monitor uncertain and diverse IoT data. M-DB constitutes of three components:(1) model-based operators (MBO) as data management abstractions for IoT application developers to integrate data from diverse sensors. Model-based operators can support event-detection and statistical aggregation operators,(2) M-Stream, a dataflow pipeline that combines model-based operators to perform computations reflecting the uncertainty of underlying data, and (3) M-Store, a storage layer separating the computation of application logic from physical sensor data management, to effectively deal with missing or delayed sensor data. M-DB is designed and implemented over Apache Storm and Apache Kafka, two open-source distributed event processing systems. Our illustrated application examples throughout the paper and evaluation results illustrate that M-DB provides a realtime data-processing architecture that can cater to the diverse needs of IoT applications.