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In this paper we propose a system for localization in a Wi-Fi environment where the exported information is used in m-commerce applications [2–4] via techniques of data mining [29,31]. The system uses the map of the field of interest and by preprocessing it, extracts a graph of the positions a user can be, along with the possible transitions between these positions. This map is used for the localization process along with the particle filtering [11] Monte Carlo technique, which attempts to extract the noise from the measurements collected during the process. In addition to this we apply novel inside-the-particle-filtering-process techniques and propose a computational system that listens to the wireless environment in order to augment the overall system’s performance. Having the extracted positioning information, m-commerce targeted messages are sent to the users. Additionally, the spatio-temporal data are processed by data mining techniques in order to export the correlations between the users’ locations.
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