IoT-Based Indoor Position Identification System using Wi-Fi Signals
DOI:
https://doi.org/10.15662/IJEETR.2026.0802044Keywords:
IoT, Indoor Positioning, Wi-Fi, RSSI, Fingerprinting, IEEE 802.11Abstract
: Indoor position identification has become an essential requirement in modern Internet of Things (IoT) environments where Global Positioning System (GPS) signals are unavailable or unreliable. GPS-based systems fail to provide accurate localization inside buildings due to signal attenuation caused by walls, ceilings, and other structural obstructions. Traditional indoor positioning techniques often suffer from limited accuracy, signal instability, and lack of real-time monitoring capabilities. Manual monitoring of indoor movement is inefficient, time-consuming, and prone to errors, particularly in large environments such as colleges, offices, hospitals, and commercial buildings. To overcome these challenges, the proposed system introduces an IoT-based indoor position identification framework using Wi-Fi signals and Received Signal Strength Indicator (RSSI) fingerprinting techniques. The system collects RSSI values from multiple Wi-Fi access points operating under the IEEE 802.11 standard to estimate indoor locations accurately. Raspberry Pi devices are used to facilitate Wi-Fi communication and data acquisition. The collected signal data is stored in an IoT cloud database for real-time monitoring and analysis. The fingerprinting approach compares real-time RSSI values with pre-collected reference data to determine precise indoor positions. The system is designed to be low-cost, scalable, and energy-efficient without requiring additional specialized hardware. Experimental evaluation demonstrates improved positioning accuracy and reliability in indoor environments. This automated approach reduces dependency on manual supervision and enhances real-time tracking efficiency. It supports smart campus management, asset tracking, and indoor navigation applications. Overall, the proposed system represents a significant advancement in IoT-based indoor localization using Wi-Fi signal strength analysis
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