## 4 IoT Systems in the Pre-Disaster Stage
### Analysis and Applications of Sensor Data  
Firstly, we tend to emphasize that data pre-processing is important to improve the efficiency of data analysis. Some missing sensor data were recorded as zero because of the irregular data transmission and the irregular observation time. We could not distinguish this missing data from an observed zero value. To solve this problem, [PERSON] and [PERSON] [75] suggested some quality management for sensor networks, such as data pre-processing (time allocation and filling short gaps in missing data), physical limit check, climate range check, internal consistency check, persistence check, step check, spatial consistency check, spatial outlier check, and data reconstruction using spatial and temporal gap-filling. In addition, [PERSON] and [PERSON] [62] suggested using a divide-and-conquer approach to process the high-dimensional data inputs from sensors. For example, we can group the sensors by their physical locations and customize the model to process each sensor. Since sensors have limited resources, another way to improve the efficiency of data analysis is to combine fog computing and cloud computing [41; 85]. To be specific, sensors send the data to the fog periodically [85]. After the fog pre-processes the data, it will be transmitted to the cloud [85]. Therefore, fog computing is mainly responsible for concentrating, distributing, caching, and analyzing the data, detecting abnormalities, analyzing the data on a smaller scale, sending notifications and feedback, and forwarding summarized data to the cloud periodically [41; 85]. Fog computing can reduce the latency of the service, respond to any emergency change immediately, and reduce the burden on the cloud [41; 85]. Cloud computing is responsible for combining and permanently storing all the data in the system to obtain a general view of the monitored environment [41; 85]. In addition, the cloud accumulates some historical data over time, which can provide important information about the weather in each area [85]. We can also run machine learning algorithms on the historical data to form a smart classifier [85]. In a word, we can use fog computing to analyze a small range of data for the timely detection of disasters and warnings. Cloud computing builds long-term predictive models by analyzing data on a larger scale. In addition, cooperation between fog computing and cloud computing can improve the efficiency of data analysis through data pre-processing in fog computing and database construction in cloud computing. Regarding data analysis methods, we can use some advanced techniques, such as machine learning [38; 45; 62; 70; 85; 86; 87], deep learning [46; 73], and time-series data analysis [58]. Some studies may use traditional methods, such as mathematical modeling (e.g., Markov Process, Laplace Transformation) [68], observing the signal (e.g., flare marker buoys) [76],and comparing the current situation with past cases [84]. Table 2 shows data analysis methods for different types of disasters. Machine learning techniques are commonly used to analyze the data from sensors. We can use machine learning techniques to analyze the data related to different types of disasters, such as earthquakes [38] and floods [45; 62; 70; 85; 86; 87]. The machine learning  
\begin{table}
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techniques used in earthquake detection include convolutional neural networks [38] and recurrent neural networks [38]. They can analyze the data collected by accelerometers. The machine learning techniques used in flood detection include Bayesian Learning [45], Multi-Layer Perceptron Artificial Neural Networks [45], Random Forest [45], J 48 Decision Tree [45], Random Tree [45], Simple Cart Decision Tree [45], and BFTree [45]. They can classify and analyze the water level data.