Study on Parallel Compressed Sensing for Mass Data in Internet of Things

Internet of Things is the network of interconnection between people and things, and between things themselves by embedding additional gadgets, such as sensors, RFID tags. Mass data are usually collected, transmitted, processed and stored in Internet of Things. In this paper, a novel sampling method, i.e. compressed sensing, is used in processing mass data in Internet of Things. Compressed sensing can significantly lower the network communication burden by reducing sampling rates of sensors, but it is nonadaptive and its algorithm is high computational complexity. For this reason, we introduce redundant dictionary into compressed sensing for increasing the flexibility and put forward the idea that is parallel processing of compressed sensing algorithm in order to improve computing speed in this paper. In the latter part of this paper, we describe the framework of parallel compressed sensing algorithm and point out our current experimental results and future work.