An Autonomous System for Indoor Positioning in Wireless Networks
Abstract: As the presence of wireless mobile devices increases a whole new aspect of services that can be provided to users emerge. Real world contexts such as position play only a marginal role in traditional computing since the user is all but bound to a single fixed location but are very prevalent in mobile computing where the user's position might change from one moment to the next. The number of services that can be offered to the users of mobile devices based on position is endless, from locating the origin of a distress call to finding friends and relatives.In this thesis, a system for positioning users in a wireless network using only the existing network infrastructure is presented. The wireless network infrastructure is made up by access points, or base stations, which function as bridges between wireless devices and the regular wired network. By analyzing the degradation of the signal broadcasted from these access points the approximate distance between the access point and the mobile device can be created. The set of this information from all detectable access points at a specific position is called a signal space location and is a unique fingerprint for every real world location. By inserting these signal space locations into an undirected graph the system becomes aware of the surroundings.Autonomy is a central concept to this method. For a positioning system to be usable in a global context instead of just a local one it needs to be able to independently analyze and adapt to new surroundings as well as detect any changes to the signal space of a building. It also needs to be able to adapt to errors, such as an access point malfunctioning. Basically, autonomy represents the ability to independently handle any situations that might change the system's view of the surroundings.Empirical tests of the method yielded discouraging results. Due to the complex nature of indoor radio wave propagation the signal fluctuated, making it difficult for the system to distinguish between signal space locations. The reason for this is the simplified signal propagation model used in the system, and with a better model many of these problems would be overcome.
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