RSS-Based Indoor Positioning Utilizing FireFly Algorithm in Wireless Sensor Networks

The ongoing decrease in the cost of handheld wireless devices, coupled with the growing demand for these, has increased the demand for localization applications. Consequently, position estimation is a problem that has been addressed using a variety of technologies. Owing to the simplicity of radio signal strength (RSS) methods, the most recent research on localization using wireless sensor networks uses RSS-based algorithms. Our present study comes up with a novel positioning technique utilizing RSS method and a meta-heuristic optimization, namely, Firefly algorithm (FFA). Meta-heuristic algorithms are proven to outperform the traditional gradient based algorithms for optimization problems. The first contribution of this work is involving FFA in RSS-based positioning. Secondly, involve Kalman Filtering in position prediction, and for barometer sensor measurements for attitude estimation. The experimental results show that the proposed algorithm can accurately determine the position with error distance less than two and half meters.