Anton Sizo

ABSTRACT Much effort has been expended to develop and improve indoor positioning. Many wireless sensor technologies have been used for indoor positioning systems; however WiFi has been the most widely employed sensor system as an alternative to Global Positioning System (GPS). Many commercial indoor positioning services such as those developed for and available on Apple and Android systems are hardly satisfying users' demand, primarily because of their inaccurate positioning. The Saskatchewan Enhanced Positioning System (SaskEPS) has been developed to provide reliable indoor positioning as a complement to GPS. SaskEPS successfully produces very reliable 2.5-Dimensional positioning (X-Y and floor) information at randomly selected fixed locations across an extensive indoor environment at the University of Saskatchewan. SaskEPS produces GPS-like positioning accuracy (sub 10 metre error) during testing; however there are several additional limitations that reduce the ability of non-GPS systems to provide accurate and reliable positioning indoors as compared to GPS. SaskEPS and other trilateration-based WiFi-based Positioning Systems can improve their positioning abilities with techniques commonly used in GPS-based positioning systems; therefore, SaskEPS has integrated a map-matching technique (Post-positioning correction) with its trilateration-based algorithm (Pre-positioning determination). In this paper we explore some limitations for WiFi-based indoor positioning with an explicit examination of SaskEPS in a complex multi-building environment. As well, some add-on localization functionalities are tested for reducing positioning errors and increasing reliability of SaskEPS.

The popularization of tracking devices, such as GPS, accelerometers and smartphones, have made it possible to detect, record, and analyze new patterns of human movement and behavior. However, employing GPS alone for indoor localization is not always possible due to the system's inability to determine location inside buildings or in places of signal occlusion. In this context, the application of local wireless networks for determining position is a promising alternative solution, although they still suffer from a number of limitations due to energy and IT-resources. Our research outlines the potential for employing indoor wireless network positioning and sensor-based systems to improve the collection of tracking data indoors. By applying various methods of GIScience we developed a methodology that can be applicable for diverse human indoor mobility analysis. To show the advantage of the proposed method, we present the result of an experiment that included mobility analysis of 37 participants. We tracked their movements on a university campus over the course of 41 days and demonstrated that their movement behavior can be successfully studied with our proposed method.

With the increasing availability of tracking technology, researchers have new tools for examining patterns of human spatial behavior. However, due to limitations of GPS, traditional tracking tools cannot be applied reliably indoors. Monitoring indoor movement can significantly improve building management, emergency operations, and security control; it can also reveal relationships among spatial behavior and decision making, the complexity of such spaces, and the existence of different strategies or approaches to acquiring and using knowledge about the built environment (indoors and out). By employing methods from computer science and GIS we show that pedestrian indoor movement trajectories can be successfully tracked and analyzed with existing sensor and WiFi-based positioning systems over long periods of time and at fine grained temporal scales. We present a month-long experiment with 37 participants tracked through an institutional setting and demonstrate how post-processing of the collected sensor dataset of over 36 million records can be employed to better understand indoor human behavior.