Calibrating Indoor Positioning Systems with Low Efforts

Recently, the positioning techniques based on the IEEE 802.11 signal strength are becoming the dominant solutions in the mobile device localization within indoor scenarios. Such solutions are characterized by two main pitfalls that compromise their effective usage in real application environments. First, during the calibration, a large amount of manual effort is required for acquiring a massive collection of training samples. Second, the positioning accuracy is directly related to the deployment of the wireless access points into the workspace, which is extremely time-consuming and requires human intervention. This paper presents an approach to reduce the manual calibration and to optimize the positioning accuracy, by selecting the best deployment schema of the wireless access points. The approach has been implemented in a tool, which uses an analytical signal propagation model to build the radio map of a given workspace, and exploits a multi-objective genetic algorithm to identify the best access points placement pattern that fits the required accuracy. A detailed experimental campaign is presented in order to show the benefits achievable by the proposed approach.