## 5 IoT Systems in the Post-Disaster Stage
### Analysis and Applications of Sensor Data  
People focus on analyzing position data and health data in order to plan evacuation routes [38; 90], allocate ambulance vehicles [95], understand the health of trapped individuals [90], and integrate information for rescuers [91; 92]. For example, [PERSON] et al. [98]can detect COVID-19 cases in a disaster by analyzing people's temperatures with machine learning techniques. Regarding locating people in disasters, [PERSON] et al. [104] used the least squares method to improve the generalized geometric triangulation scheme, which allows sensors to track the position of rescuers or victims. Similarly, [PERSON] et al. [105] proposed a cooperative location inference mechanism to locate the sensors automatically. And they developed a user-participatory sensing environment that allows people to collect position data from sensors. In addition, we may carry out post-disaster activities with the cooperation of multiple agencies [9]. Thus, we need to collect data from various heterogeneous sensors. [PERSON], [PERSON], [PERSON] and [PERSON] [105] proposed a novel multi-factor cost model to integrate the multi-modal sensor data consistently and flexibly. [PERSON] et al. [106] introduced a semi-automated role mapping process for dynamic cross-domain accesses of sensors in post-disaster relief to solve heterogeneity and protect sensitive information. In addition, the combination of fog computing and cloud computing can improve the efficiency of data analysis [90; 91; 92]. For example, [PERSON] et al. [91] proposed the Sieve, Process, and Forward (SPF) Fog-as-a-Service platform to address the scenario of post-disaster relief. Fog computing is also helpful in reducing the time of search and rescue since it can improve the efficiency of data analysis through pre-processing some data analysis, such as data categorization and data novelty analysis [90; 91]. Cloud computing aims to store the data and process deeper data analysis [90; 91; 92; 98]. The fog layer exists in the gateway (e.g., drones and evacuation vehicles) and serves as a bridge between the sensor layer and cloud layer of the Internet of Things [90; 91]. The utilization of the fog layer is attributed to its position awareness and close proximity to the sensors [90]. This enables it to perform essential data pre-processing tasks, such as data categorization, novelty analysis, panic health status classification, and alarm creation [90]. Due to the inherent limitations in computing and storage capacities of the fog layer, the cloud layer was employed to store and analyze environmental and health data, as well as the corresponding panic health status data [90]. This facilitated the generation of alerts in the form of compiled medical records [90]. The Cloud layer includes temporal data mining, cloud storage, panic health sensitivity monitoring, evacuation strategy, and evacuation map building [90]. Thus, we usually develop an application to combine the data and conduct big data analysis on the cloud [91; 92]. And we will place the mobile decision-making centers for post-disaster rescue on the cloud [91; 92]. Regarding data application, scholars usually publish the results of data analysis on the web application [15; 38; 89; 91; 92; 99; 105]. Some of them are mobile applications [15; 92; 105]. We visualize data with maps to present the collapse, the traffic, the shelters, and the evacuation route in the post-disaster stage [15; 38; 88; 90; 91; 92]. For example, [PERSON] et al. [88]'s City Geospatial Dashboard can provide road congestion information to help disaster response teams estimate travel times to reach disaster areas. toRoute planning is one of the major applications in the post-disaster stage [38]. Based on position data, [PERSON] et al. [38]'s system can provide a real-time evacuation route guidance service, including searching for the safest shelter and showing pedestrian paths for users. Then, previous studies tend to use dynamic programming methods to support route planning. [PERSON] et al.