Sensors Positioning for Reliable RSSI-based Outdoor Localization using CFT

The sensors' geographical position is vital information in wireless sensor networks (WSNs) required for tracking, target detection, controlling, and monitoring systems. Thus 10-calization is an essential matter in WSNs. Localization methods are classified into two categories, range-based and range-free. Range-based localization achieves higher accuracy using received signal strength indicator (RSSI) values compare to range-free approaches, which obtain lower efficiency. RSSI produces a convenient process to find sensors' positions because of the energy constraints of hardware in sensors. However, for the channel noise, fading, and shadowing, it is not possible to calculate the actual locations. In this paper, we introduce an RSSI-based localization scenario that considers the trend of RSSIs obtain from the Wi-Fi access point (AP) to estimate the positions of stations (STAs), i.e., sensors. Through applying the curve fitting technique (CFT), we estimate the relationship and choosing the best perfect curve the relation is established. The error level can significantly reduce by selecting the best accurate curve. Simulation demonstrates that the proposed localization scheme achieves higher positioning accuracy compared to a broad range of localization approaches.