With the continuous development of the Chinese economy and the gradual acceleration of urbanization, it has caused tremendous damage to the environment. The bad air environment seriously damages the physical and mental health of the people. The change in smog concentration will be affected by many realistic factors and exhibit nonlinear characteristics. The method proposed in this paper is to use the Internet of Things (IoT) technology to monitor the acquired data, process the data, and predict the next data using a neural network. The existing prediction models have limitations. They don't accurately capture the law between the concentration of haze and the factors affecting reality. It is difficult to accurately predict the nonlinear smog data. One algorithm proposed in this paper is a two-layer model prediction algorithm based on Long Short Term Memory Neural Network and Gated Recurrent Unit (LSTM&GRU). We set a doublelayer Recurrent Neural Network to predict the PM2.5 value. This model is an improvement and enhancement of the existing prediction method Long Short Term Memory (LSTM). The experiment integrates data monitored by the IoT node and information released by the national environmental protection department. First, the data of 96 consecutive hours in four cities were selected as the experimental samples. The experimental results are close to the true value. Then, we selected daily smog data from 2014/1/1 to 2018/1/1 as a train and test dataset. It contains smog data for 74 city sites. The first 70% of the data was used for training and the rest for testing. The results of this experiment show that our model can play a better prediction.
Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment, especially in resource-constrained sensor networks, such as the perception layer of Internet of Things applications. However, in all existing single chaining watermark schemes, how to ensure the synchronization between the data sender and the receiver is still an unsolved problem. Once the synchronization points are attacked by the adversary, existing data integrity authentication schemes are difficult to work properly, and the false negative rate might be up to 50 percent. And the additional fixed group delimiters not only increase the data size, but are also easily detected by adversaries. In this paper, we propose an effective dual-chaining watermark scheme, called DCW, for data integrity protection in smart campus IoT applications. The proposed DCW scheme has the following three characteristics: (1) In order to authenticate the integrity of the data, fragile watermarks are generated and embedded into the data in a chaining way using dynamic grouping; (2) Instead of additional fixed group delimiters, chained watermark delimiters are proposed to synchronize the both transmission sides in case of the synchronization points are tampered; (3) To achieve lossless integrity authentication, a reversible watermarking technique is applied. The experimental results and security analysis can prove that the proposed DCW scheme is able to effectively authenticate the integrity of the data with free distortion at low cost in our smart meteorological Internet of Things system.
Internet of Things (IoT) is a network that connects things in a special union. It embeds a physical entity through an intelligent perception system to obtain information about the component at any time. It connects various objects. IoT has the ability of information transmission, information perception, and information processing. The air quality forecasting has always been an urgent problem, which affects people's quality of life seriously. So far, many air quality prediction algorithms have been proposed, which can be mainly classified into two categories. One is regression-based prediction, the other is deep learning-based prediction. Regression-based prediction is aimed to make use of the classical regression algorithm and the various supervised meteorological characteristics to regress the meteorological value. Deep learning methods usually use convolutional neural networks (CNN) or recurrent neural networks (RNN) to predict the meteorological value. As an excellent feature extractor, CNN has achieved good performance in many scenes. In the same way, as an efficient network for orderly data processing, RNN has also achieved good results. However, few or none of the above methods can meet the current accuracy requirements on prediction. Moreover, there is no way to pay attention to the trend monitoring of air quality data. For the sake of accurate results, this paper proposes a novel predicted-trend-based loss function (PTB), which is used to replace the loss function in RNN. At the same time, the trend of change and the predicted value are constrained to obtain more accurate prediction results of PM 2.5 . In addition, this paper extends the model scenario to the prediction of the whole existing training data features. All the data on the next day of the model is mixed labels, which effectively realizes the prediction of all features. The experiments show that the loss function proposed in this paper is effective.
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