1 Positioning Awareness: an Essential Component for Mobile Multimedia Applications F. Lassabe, P. Canalda, P. Chatonnay, F. Spies LIFC - Laboratoire d’Informatique de l’Universit´ e de Franche-Comt´ e Num´ erica - Multimedia Developpement Center Cours Louis Leprince Ringuet, BP 21126 25201 Montb´ eliard Cedex, France Email: {frederic.lassabe,philippe.canalda,pascal.chatonnay,francois.spies}@pu-pm.univ-fcomte.fr D. Charlet INRIA Rocquencourt Email: damien.charlet@inria.fr Abstract The spreading of the WiFi networks allows new applica- tions. New problems bound to the mobility of the terminals arise. In this article, we deal with the mobil terminal positioning. The positioning service is integrated in a mo- bility management middleware. The solution proposed is trilateration for which the distances are computed accord- ing to the signal strength. We also propose some method to refine positioning in order to increase the precision. The positioning accuracy is evaluated by a large set of tests. Mobil terminal positioning is the first step to context awareness. Then we introduce prediction using Hidden Markov Model and how we use the past to determine the futur. Finaly we show how we use positioning and prediction in two multimedia applications Keywords: Mobility Management Middleware, WiFi Positioning, Handover, Trilateration, Friis-based calibrated model, Predic- tion, Hidden Markov Model. I. Introduction We call position awarness the knowing of the position and the probable futur position of a mobil terminal. Both of these are really usefull in the context of the spreading of wireless networks and their associated services. In particular, the continuity of multimedia services provided must be ensured in mobility. The mobility prediction is a potential technique to anticipate problems arising when a mobil terminal moves from one antenna to another (ie during the handover). Position could also leed to the definition of new services. Wireless networks are of various types: GSM, UMTS, WiFi [1], etc. The services provided are also numerous, from consulting web pages to watching on-demand video sequences. Because of these facts, it is natural to consider using a middleware to provide the mobility management. This way, the interface between the user’s applications and the service continuity component is transparent. In this article, we present an indoor WiFi positioning and prediction system which is part of a service which aims at ensuring service continuity. this service relay on mul- tiple components: a system learning the mobile terminal moves and a system using the data acquired to anticipate the service interruptions. The handover is managed by a protocol dedicated to the mobility. In this article we focus on two step : the positioning and the prediction. The service presented comes within the scope of a streaming platform of multimedia content. This project name is MoVie [2]. It is composed of 4 modules (fig. 1): • NetMoVie integrates the RTP/RTCP protocol. It re- ceives a few video sequence qualities and selects the most adapted one depending on the current situation. • SysMoVie gathers ORB components and integrates the hierarchy of video caches. The strategy of video cache management is specific to the particular tem- poral data. • WebMoVie represents a query interface of the MoVie platform. It is the entry point of clients where they are identified. A trader is used for each query in order