Multi-source big data dynamic compressive sensing and optimization method for water resources based on IoT

Water resources data is usually featured by huge volume, multi-variable as well as multi-dimension, which leads to deficiency in terms of data integrity, rationality and efficacy in water resources management and allocation. In order to reduce the amount of data collected in the Internet of things, to improve the processing speed for water resources data, a constrained single objective optimization problem is transformed into a multi-objective optimization problem with sparse degree as the optimization objective in compressed sensing reconstruction, and then a sparse reconstruction method based on hybrid multi-objective optimization is proposed. The algorithm is designed based on the multi objective optimization problem, and the algorithm is easy to implement and adjust. Application results show that the proposed multi-objective particle swarm optimization-Genetic algorithm (MOPSOGA) is than traditional gradient projection sparse reconstruction algorithm (GPSR-BB) algorithm iterations decreased 43.9%. The success rate of reconstruction is higher than that of the traditional algorithm of 0.16; it's with a better reconstruction performance. (C) 2017 Elsevier Inc. All rights reserved.