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Reliable RSSI Trend based Localization for three Different Environments

In our modern society, WiFi (wireless fidelity) is an essential part of human life because every smartphone builds with WiFi facilities. Due to the requirement for WiFi, the wireless sensor networks (WSNs) gaining its maturity and the data traffic demand has also increased. Therefore, the problem of network localization became more challenging. For any uncertain position localization by the signal strength, the received signal strength indicator (RSSI) is a crucial benchmark. The localization process using RSSI is simple as well as cheap than any other existing methods. At the time of the internet of things (IoT) applications, WiFi can use as a leading access technology. Thus we focused on the localization using the cheapest WiFi module NodeMCU (ESP8266), which is easily programmable and operate on 2.4 GHz frequency bands. For position estimation, two modules have used, one as an access point (AP) and another as a station (STA). In the present literature, a lot of techniques have discussed to reduce the error level. However, most of them cannot achieve significant accuracy. Therefore, in this work, we proposed a method by curve fitting technique (CFT), which reduce error to an extreme limit. Moreover, we observed the error in three circumstances, i.e. outdoor, corridor and indoor. Experimental results demonstrate that CFT provides a better estimation than other existing models.