Tools for modelling distance estimation based on RSSI

The systems for localization of resources in indoor environments based on Received Signal Strength Indicator (RSSI) are widely used today. Since satellite navigation systems, such as GPS or GLONASS, have certain difficulties in the indoor environments, the signals of deployed wireless devices, such as sensor nodes, access points etc, are used for localization instead. Those systems are known as Indoor Positioning System (IPS). Those systems are used for resource tracking and positioning in places such as airports, railway stations, shopping malls, warehouses, production facilities, construction sites, and healthcare institutions. The Bluetooth Low Energy is one of the wireless technologies that can be used with great efficiency for indoor localization. It offers easy and economic implementation on mobile devices such as smart phones and tablets. There are many techniques used for determination of position. In a number of methods, such as ROCRSSI or MinMax, the distance from the wireless nodes is used for calculating the location. In those systems the main challenge is to accurately estimate distance from the device based on signal strength. In this paper, usability of various software tools for modelling the distance estimation based on RSSI is discussed. Those software tools are Microsoft Access, R Studio, Octave, and Python.