The main aim or goal of our research work is to localize the workers working in the mining area exactly or with minimum localization error. Network formation in mining areas is always very crucial. Laborers working in mining areas need strong availability of network as when they go down or deep in a mining area they can be rescued easily. It can only be possible when we know the exact location of the worker working in the particular area. For this, we need a better localization scheme. Many recent developments have been made in the field of the mining area. Random forest scheme, SVM-based regressive localization, Wi-Fi-based localization, and these are some schemes developed so far. RSSI and Trilateration work for both indoor and outdoor localization. The difference is only in terms of temperature because indoor temperature is different from outdoor temperature. When we are working on the basis of distance and signal strength then the proposed localization algorithm is suitable for hill areas too. From the results of the simulation, the new localization algorithm proposed in the paper with error checking and correction increases the accuracy of the localization in the X direction is 99.98 and in Y direction is 99.97 algorithms based on RSSI and dual prediction.
