# 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
#### 3.2.3 Bluetooth-Based System for Industrial Environments  
[PERSON] et al. [60] proposed an indoor localization system that focuses on using BLE technology for tracking assets in large industrial environments with minimal infrastructure. The primary objective of the system is to achieve practical localization accuracy while maintaining simplicity and cost-effectiveness. The system leverages RSSI measurements to estimate the positions of BLE tags attached to assets, animals, or equipment. To address the challenges of dynamic and obstacle-rich industrial environments, the system adopts range-free localization techniques, which rely on relative signal strengths and geometric properties rather than direct distance measurements.  
Figure 17 shows the system architecture, which consists of several key components. BLE tags are attached to the objects to be tracked, and they continuously broadcast BLE signals. These signals are received by anchor nodes, which are fixed Raspberry Pi-based devices placed strategically across the industrial environment. The anchor nodes measure the RSSI values of the incoming signals and send these data to a central server for processing. The server uses the collected RSSI data to estimate the location of the tags using one of the proposed localization algorithms.  
Three localization algorithms are employed to cater to different levels of complexity and accuracy requirements. The Ring Algorithm converts RSSI measurements into distance ranges (or rings) around each anchor. The location of the tag is estimated based on the intersection of these rings, with weights assigned according to the reliability of each signal. The Sectional Algorithm divides the tracking area into predefined zones and estimates the tag's location by identifying the zone with the strongest signal strength. This method is particularly useful when precise positioning is not required. The Mass Algorithm treats the RSSI values as weights, calculating the tag's position as the center of mass of these weighted signals, striking a balance between simplicity and accuracy.  
To improve the reliability of localization, the system aggregates RSSI measurements over 30-s time windows to mitigate the impact of signal variations. Figure 18 shows the  
Figure 17: Field testing of localization algorithms in a cattle monitoring framework.  
evaluation area with anchor and section markings of the system module. This approach reduces inaccuracies caused by multipath propagation, obstacles, and dynamic changes in the environment. The system was tested in a 1600 m\({}^{2}\) cowshed, where 10 anchors were deployed to track BLE tags attached to cows.  
The real positions of the cows were manually recorded to evaluate the system's accuracy. Despite challenges such as signal attenuation caused by the animals' bodies and the presence of obstacles, the system achieved an average localization error of less than 8 m. Figure 19 illustrates the process of handling aggregated measurements and inputting localization data, showing how the system processes and integrates sensor information for accurate position estimation [60].  
The methodology addresses the key challenges of industrial environments, such as dynamic changes, limited infrastructure, and signal interference. By using a minimal number of anchors and adopting robust algorithms, the system ensures cost-effective deployment and reliable tracking in challenging conditions. This BLE-based indoor localization system provides a practical solution for industries seeking to enhance asset management and operational efficiency without extensive infrastructure investments.