Multi-resolution data communication in wireless sensor networks

There is an increasing trend in using data collected by sensor devices to enable better understanding of the physical world for humans and support the creation of pervasive environments for a wide range of applications in different domains such as smart cities, and intelligent transportation. However, the deluge of data created and communicated and the low-processing capabilities of the used sensor devices lead to bottlenecks in the processing and interpreting of the data. We introduce a data reduction approach that submits high-granular data in times of high activity in the sensor readings and low-granular data in times of low activity. We consider and discuss different methods to measure activity in the data and modify the symbolic aggregate approximation algorithm that uses a fixed window length to adapt the length according to the data activity for ultimately less data communication between sensor node and sink/gateway. We evaluate our approach over real-world data sets and show that reduction of data size while maintaining the features of the data can be achieved.