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In order to mitigate the challenges of air pollution prevention and improvement, a large number of Internet of Things (IoT) related technologies have been developed to evaluate and monitor various parameters of air quality. This paper reviews the fundamental characteristics of IoT technologies and accordingly proposes an intelligent and multifunctional IoT monitoring platform to prevent and improve air pollution. The techniques of radio frequency identification (RFID), M2M and sensor network were discussed and compared. To improve the ambient air quality more efficiently, the comprehensive network communication system, cloud decision system, tracking information system, and online management system should be well established using IoT technologies. Meanwhile, we also discussed several cases verifying the availability and feasibility of the performance of the smart ambient air quality management platform on the IoT basis.

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Current mainstream super-resolution algorithms based on deep learning use a deep convolution neural network (CNN) framework to realize end-to-end learning from low-resolution (LR) image to high-resolution (HR) images, and have achieved good image restoration effects. However, as the number of layers in the network is increased, better results are not necessarily obtained, and there will be problems such as slow training convergence, mismatched sample blocks, and unstable image restoration results. We propose a preclassified deep-learning algorithm (MGEP-SRCNN) using Multilabel Gene Expression Programming (MGEP), which screens out a sample sub-bank with high relevance to the target image before image block extraction, preclassifies samples in a multilabel framework, and then performs nonlinear mapping and image reconstruction. The algorithm is verified through standard images, and better objective image quality is obtained. The restoration effect under different magnification conditions is also better.The gradient descent method continuously adjusts the weights to obtain a mapping from LR to HR. Its simple network structure design and excellent image restoration results created a precedent for deep learning in super-resolution image reconstruction. However, the SRCNN method does not obtain better results when the number of layers in the network is increased. The training convergence rate is slow and it is not suitable for multiscale amplification.Based on the above problems, we propose an improved deep learning algorithm for Gene Expression Programming (GEP) preclassification. This method uses the GEP multilabeling algorithm to classify the trained image set and select a subset of samples that are related in color and texture feature categories, thereby reducing the complexity of the convolutional neural network parameters. We compare the performance of our approach to that of other state-of-the-art methods and obtain improved objective image quality. Related WorkIn recent years, deep learning and artificial intelligence have been widely used in various industries, especially in the field of computer vision, and have achieved better results than traditional methods [14,15]. Using the feed-forward depth network methods of CNN is the mainstream of the current super-resolution reconstruction field after sparse representation. Such methods focus less on the reconstruction speed and more on whether the high-resolution map can be better restored at a large magnification. They have better generalization ability and ability to characterize the high-level characteristics, compared with traditional shallow learning algorithms. SRCNN/Fast SRCNN (FSRCNN)Dong et al. [5] first proposed the use of a convolutional neural network for image super-resolution reconstruction. An LR image was first enlarged to the target size using Bicubic interpolation, and then a nonlinear mapping was performed through a three-layer convolutional network. The obtained results were output as high-resolution images, and good results were obtained. As s...

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