Towards Modelling and Reasoning about Uncertain Data of Sensor Measurements for Decision Support in Smart Spaces

Smart Spaces currently benefits from Internet of Things (IoT) infrastructures in order to realise its objective. In many cases, it demonstrates this through certain automated applications that relies on sensor streams that comes with some uncertainties in measurements. However, these sensor data tend to be uncertain or fault-prone due to the faults of the sensor either themselves or the wireless sensor networks. Sometimes, the extreme operating condition of the sensor can be a contributing factor to the uncertainty. The proposed approach provides a software framework that aims at homogenising, annotating and reasoning over these data. The framework consists of four layers that utilizes the semantic process involving a domain ontology and reasoning process to deliver improved quality data streams to applications. This will allow for early detection of missing data points and enhancing the accuracy of decisions and actions in such spaces.