BIG DATA DYNAMIC COMPRESSIVE SENSING SYSTEM ARCHITECTURE AND OPTIMIZATION ALGORITHM FOR INTERNET OF THINGS

In order to reduce the amount of data collected in the Internet of things, to improve the processing speed of big data. To reduce the collected data from Internet of Things by compressed sensing sampling method is proposed. To overcome high computational complexity of compressed sensing algorithms, the search terms of the gradient projection sparse reconstruction algorithm (GPSR-BB) are improved by using multi-objective optimization particle swarm optimization algorithm; it can effectively improve the reconstruction accuracy of the algorithm. Application results show that the proposed multi-objective particle swarm optimization-Genetic algorithm (MOPSOGA) is than traditional GPSR-BB algorithm iterations decreased 51.6%. The success rate of reconstruction is higher than that of the traditional algorithm of 0.15; it's with a better reconstruction performance.