A study on wireless sensor network based indoor positioning systems for context‐aware applications

The newer context‐aware applications require many inputs and amongst them the location information is one of the most important. In the near future, we see the potential in using wireless sensors deployed inside buildings to support in generating the location information besides their other routine tasks. This paper records the efforts involved in designing and prototyping a centralized indoor positioning system for tracking a target's position. We present an in‐depth discussion on the RSSI based positioning algorithms both, range‐based and range‐free. Considering that the signal strength can be distorted heavily due to multi‐path and shadowing, we have proposed the weighing schemes to leverage the credibility of the measured RSSI values. For online tracking, we have also proposed boundary selection and local grid scanning to lower the searching time, and the RSSI data dissemination and collection schemes to reduce traffic overhead. Evaluations have been done in both the field measurements and with an RSSI generator. The generator has been developed to simulate and replace the real measurements for the ease of algorithm design and testing, thus avoiding repetitive field measurements. The results show that positioning systems with adequate accuracy can be built with our proposed schemes. We expect that these proposed schemes to be integrated in multitude of systems with context‐aware applications, which use location information.