Automatic Mitigation of Sensor Variations for Signal Strength Based Location Systems Mikkel Baun Kjærgaard Department of Computer Science, University of Aarhus, IT-parken, Aabogade 34, DK-8200 Aarhus N, Denmark, Email: mikkelbk@daimi.au.dk Abstract. In the area of pervasive computing a key concept is context- awareness. One type of context information is location information of wireless network clients. Research in indoor localization of wireless net- work clients based on signal strength is receiving a lot of attention. How- ever, not much of this research is directed towards handling the issue of adapting a signal strength based indoor localization system to the hard- ware and software of a specific wireless network client, be it a tag, PDA or laptop. Therefore current indoor localization systems need to be man- ually adapted to work optimally with specific hardware and software. A second problem is that for a specific hardware there will be more than one driver available and they will have different properties when used for localization. Therefore the contribution of this paper is twofold. First, an automatic system for evaluating the fitness of a specific combination of hardware and software is proposed. Second, an automatic system for adapting an indoor localization system based on signal strength to the specific hardware and software of a wireless network client is proposed. The two contributions can then be used together to either classify a spe- cific hardware and software as unusable for localization or to classify them as usable and then adapt them to the signal strength based indoor localization system. 1 Introduction In the area of pervasive computing a key concept is context-awareness. One type of context information is location information of wireless network clients. Such information can be used to implement a long range of location based services. Examples of applications are speedier assistance for security personnel, health- care professionals or others in emergency situations and adaptive applications that align themselves to the context of the user. The implementation of speedier assistance could, for example, come in the form of a tag with an alarm but- ton that, when pressed, alerts nearby persons to come to assistance. The alarm delivered to the people nearby would contain information on where in the phys- ical environment the alarm was raised and by whom. Applications that adapt themselves to the context they are in are receiving a lot of attention in the area of pervasive computing, where they can solve a number of problems. One type