Sensor Fusion Techniques for Accurate Indoor Tracking in IoT-Based Smart Environments

Sensor fusion integrates data from multiple sensors to provide a more accurate, reliable, and contextual sense of the surrounding environment. So, it became a key method for correct monitoring in battery-dependent Internet of Things clever environments, especially Indoor tracking. There is only one dedicated sensor. To benefit the most from it and surround these weaknesses in complex indoor environments with wrongness. The sensor fusion methods for accurate indoor tracking in IoT-based Smart Environments are proposed to solve the problems above by integrating data from multiple sources, including Wi-Fi APs and BLE beacons, RFID readers, and IMUs. This results in a more complete and detailed view of the environment crucial for location-based applications such as asset tracking, localization, or navigation within smart buildings. There are many algorithms to fuse sensor data. The algorithms process the sensor data and give a fused result that outperforms all of these individual components. Sensor fusion in IoT-based smart environments improves indoor tracking accuracy, robustness, and scalability. It also permits real-time tracking localization of objects and people; it is the key to optimally managing smart environments. The sensor fusion method generally improves indoor tracking properties in the rapidly growing IoT-based smart environments.