# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
### Transforming Industrial Operations with Indoor Localization Technologies
#### 3.2.4 Fast and Robust Wi-Fi Localization Methodology for Dynamic Industrial Environments  
[PERSON] et al. [61] proposed a Wi-Fi-based localization system that offers accurate and fast indoor positioning for large and dynamic environments, such as exhibition venues. Figure 19: Aggregated measurement handling and localization input process. Figure 18: Layout of the evaluation area with anchor locations (blue circles) and section markings (numbered squares). Blue circles indicate anchor points (1–10), while numbered squares (1–20) represent predefined sections used for localization and tracking analysis. The system leverages RSSI values from Wi-Fi signals to estimate the positions of mobile devices. This approach minimizes setup time, employs customized localization models, and uses dynamic resolution control to balance accuracy and processing load efficiently. Figure 20 shows the system architecture consists of three primary components: Wi-Fi sniffers, mobile devices, and a central server. The Wi-Fi sniffers are ordinary Wi-Fi routers configured to detect packets transmitted by mobile devices and measure their RSSI values. These sniffers are strategically placed throughout the venue and connected to a central server through a local area network (LAN) or wireless connections. The mobile devices, such as smartphones, continuously communicate with the APs in the venue, broadcasting Wi-Fi packets. The sniffers capture the RSSI values from these packets for further processing. The central server acts as the core processing unit. It collects RSSI data from the sniffers and processes them using a customized localization model tailored to the specific characteristics of each mobile device. The server employs an expectation maximization (EM) algorithm to iteratively refine the localization model and estimate device positions. This dynamic refinement improves the accuracy of the localization by continually updating the signal strength-to-distance model based on incoming data. The system uses a piecewise linear model to convert RSSI values into estimated distances between the mobile device and the sniffers. This model consists of three linear segments, each covering different RSSI ranges, allowing for flexibility and improved accuracy in diverse environments. The model is customized for each device to account for variations in transmission characteristics. Additionally, a weighting mechanism prioritizes sniffers with stronger RSSI values, ensuring that reliable measurements have a greater impact on the final position estimate. To optimize performance, the system incorporates dynamic resolution control by dividing the venue into a grid of cells. The grid resolution can be adjusted dynamically: finer grids offer higher localization accuracy but require more processing power, while coarser grids reduce computational load, enabling the system to track more devices simultaneously. This flexibility allows the system to adapt to real-time demands and server capacity. The localization process uses a grid search optimization approach to estimate the device's position. The system searches for the grid cell that minimizes the error between the estimated distances and the actual distances to the sniffers. The search begins with the grid cell corresponding to the strongest RSSI value, which helps reduce computational complexity and improve efficiency. One of the system's key advantages is its short setup time. Unlike traditional Wi-Fi fingerprinting methods, which require extensive training data, this system does not need  
Figure 20: Framework of the proposed Wi-Fi-based localization approach. an explicit training phase. This makes it particularly useful for temporary setups, such as exhibition venues, where rapid deployment is crucial. The system's customized localization models also ensure adaptability to different mobile devices, making it robust for real-world applications. In terms of performance, the system achieves a mean localization error of approximately 1.7 m, demonstrating its accuracy and efficiency.