Enhancing Trilateration Localization by Adaptive Selecting Distances

If there is not coordinating data, the sensor parameter is of no value in the internet of things system. A promising approach to offer location parameter is to leverage a specific localization method. However, there exist some uncertain factors, which result in low accurate localization. Many research works are presented to improve localization accuracy or localization efficiency. As many of them did not consider the uncertainty effect and mechanism during localization computation, the improvement is not promising. For this problem, an enhanced trilateration localization is therefore presented to improve the localization accuracy, which is called improved trilateration localization through quality evaluating and adaptive selecting (IT-QEAS). We firstly assess the quality of distance measurement results. Then these anchor nodes with higher quality distance are optimizing selected to perform localization calculation. Lastly, high accuracy location results can be achieved. Experimental results of definite localization data demonstrate that the trilateration localization can be improved effectively.