![]() RSSI varying without a change of distance might cause more spurious triggering. Signal stability is more important when you are using the RSSI to infer distance, either directly from the RSSI itself or indirectly via, for example, the iOS immediate, near and far indicators. Firstly those with very stable RSSI and secondly those with an RSSI that had a standard deviation between about 4 and 6 dBm. We found that beacons belonged to one or two groups. We are success to reduce the average error level up to 8.32%.Smaller bars are better and represent beacons Then, we compared the estimated distance with actual distance to find the error level in percentage. First, we take around 300 sample data (RSSI values) and find the standard deviation to calculate how much the RSSI values are spread out and use curve fitting technique to find suitable equation for estimate distance. In this work we use RSSI technique to determine the distance. Time of Arrival (TOA), Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) algorithms etc). There are lots of technique to find out the distance between two nodes (e.g. To locate a device distance measurement one of most important issue. We used one node as a AP and another as a STA. NodeMCU which acts itself as a sensor node can be used as Access Point (AP) or as a STAtion (STA). In this work we used two NodeMCU (ESP8266 WiFi module) which is a easily programmable. WiFi is also applicable in future tech of IoT (Internet of Things). Airports, railways, bus-stand, home, markets everywhere now people uses WiFi because its reliability and low-cost. The use of WiFi is now a part of each human life.
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