Semi Embedded Architecture for a mobile indoor localization system

The Internet of Things (IoT) is one of the most in-demand technologies due to its ability to simplify complex tasks through low-cost devices connected to the internet. Indoor positioning is one of the most sought-after indoor technologies, as most indoor services require position information to provide high-quality, low-latency services. As a result, the indoor positioning field is also contributing to the rapid development of the IoT by using objects as sources of information to assist in tracking. This paper describes the development and evaluation of a low-cost indoor positioning approach using the Internet of Things (IoT) and received signal strength (RSS) based on UWB-OFDM technology. The proposed approach aims to simplify complex tasks and improve the quality of indoor services by providing accurate and low-latency position information. We introduce the Mobile Access Point Model (MAPM), an optimization algorithm that reduces the negative impact of multipath on the system quality through crowdsourcing. We evaluated the MAPM model in a real-world setting using a Wi-Fi node, which scans the user-reinforced access point and calculates the distance using the scanned RSSI. The node sends the necessary data to the server for software processing, and the server uses the received distances to calculate the position information and store the data in the system database. The evaluation results showed that the proposed MAPM model reduced the negative impact of multipath (reduces NLOS conditions) by 63%, and the root mean square error (RMSE) of the proposed approach using a Wi-Fi node was 69.52 cm. This paper presents a low-cost and effective indoor positioning approach using IoT devices, which can contribute to the rapid development of IoT technology.