# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
### Strategic Insights: Indoor Localization in Military Applications
#### 3.3.3 Wi-Fi-Based Two-Level Indoor Localization for Hazardous Area Monitoring  
[PERSON] et al. [63] proposed a system that utilizes a two-level Wi-Fi fingerprint-based indoor localization approach specifically designed for monitoring dangerous areas. The system aims to provide accurate and efficient real-time positioning by leveraging RSSI data collected from Wi-Fi networks. The architecture is divided into two primary phases: an offline phase and an online phase.  
In the offline phase, RSSI data are collected multiple times from various locations within the target environment to build a reliable fingerprint database, which has been shown in Figure 23 in the flowchart of both the online and offline scenarios. To minimize fluctuations caused by environmental factors such as temperature, humidity, and multipath effects, the collected data undergo Gaussian filtering for preprocessing. The filtered RSSI  
Figure 22: Detailed block diagram of system functions.  
data are then clustered into multiple sub-databases using the Affinity Propagation Clustering (APC) algorithm, which groups the data based on similarity. Instead of using traditional Euclidean distance, the clustering process employs the Shepard similarity metric, which is better suited to RSSI data due to the non-linear relationship between signal strength and physical distance.  
After clustering, each sub-database is used to train a support vector regression (SVR) model. To optimize the performance of the SVR model, parameters such as kernel width, penalty factor, and error tolerance are fine-tuned using the particle swarm optimization (PSO) algorithm. The optimized models are then stored for use during the online phase. In the online phase, real-time RSSI data are collected from mobile devices or nodes carried by personnel operating in the dangerous area. The real-time data are preprocessed in the same way as during the offline phase to ensure consistency. The localization process begins with coarse localization, where the current RSSI fingerprint is compared with the cluster centers in the fingerprint database. The sub-database that most closely matches the current data is selected for further processing.  
Once the appropriate sub-database is identified, fine localization is performed using the PSO-optimized SVR model. Figure 24 illustrates the schema and functionality of the positioning system in a chemical workshop, highlighting how the system operates to ensure accurate localization within the workshop environment. The model processes the refined data to estimate the precise location of the mobile device, achieving high localization accuracy. The system is capable of running the trained SVR model locally on portable devices such as Raspberry Pi equipped with Wi-Fi modules like ESP8266. The device can then provide real-time location updates and issue alerts if personnel enter hazardous zones.  
Figure 23: The fundamentals of localization algorithms for the online and offline scenarios.  
The overall system architecture features wireless routers as anchor points and a cloud server for clustering and model training. The trained models are sent back to the mobile device for real-time localization, ensuring portability and efficiency. This two-level approach optimizes computational complexity by narrowing the search space through clustering before performing detailed localization. The system offers several advantages, including high accuracy, efficiency, and scalability. The combination of clustering and regression techniques reduces the computational load during real-time positioning while maintaining precision. The use of portable hardware and cloud-based processing makes the system adaptable to dynamic and hazardous environments, making it a valuable tool for safety monitoring and management in dangerous areas.