Data recovery algorithm based on generative adversarial networks in crowd sensing Internet of Things

Internet of Things has developed quickly to share data from billions of physical devices. Completeness of data is important especially in crowd sensing Internet of Things. How to recover the lost data is a fundamental operation to utilize the coming of Internet of Things. Existing data recovery algorithms depend heavy on the accuracy distribution of environmental data and result in bad performance when reconstructing the lost data. This paper introduces a data recovery algorithm based on generative adversarial networks. The convolution neural network is used as the basic model of this algorithm. We add a restore network to reload the unlost data after recovery in this algorithm. The algorithm mainly includes two parts: (1) training process, in which all the collected sensory data are used to train the proposed generative adversarial networks model and (2) data recovery process, in which the lost data is recovered by using the trained generator. We use random loss dataset and periodic loss dataset to validate the data recovery performance. Finally, these two cases can verify that the recovery algorithm based on generative adversarial network is more enhanced compared with the comparison experiment under the three metrics of mean square error, mean absolute error, and R-square. The results show that our proposed algorithm can obtain data that are reliable and thus improve the performance of data recovery.