Research of Parallel Compressed Sensing for Mass Data in Internet of Things

Internet of Things is one of the most popular scientific and technical terminologies.In this paper, we use compressed sensing theory to process mass data in Internet of Things.Compressed sensing is a sampling method that data sampling and compressing can be done simultaneously.Compressed sensing can significantly lower the data size by reducing sampling rates of sensors, but its algorithm has high computational complexity and its transformation basis is nonadaptive.This paper puts forward parallel processing of compressed sensing algorithm for high computational complexity.At the same time, we introduce redundant dictionary into compressed sensing for increasing the flexibility.