Using smartphones for prototyping semantic sensor analysis systems

Abstract

The increasing usage of sensors in modern technical systems and in consumer products necessitates using efficient and scalable methods for storing and processing sensor data. Coupling big data technologies with semantic techniques not only helps achieving the desired storage and processing goals, but also facilitates data integration, data analysis and the utilization of data in unforeseen future applications through preserving the data generation context. In this work, an approach for prototyping semantic sensor analysis systems using Apache Spark is proposed. The approach uses smartphones to generate sensor data which is transformed into semantic data according to the Semantic Sensor Network ontology. Efficient storage and processing methods of semantic data are proposed and a use case where a smartphone is deployed in a transportation bus is presented along with a street anomaly detection application.