Indoor Positioning for Critical Applications
Major emergencies make chaos and confusion in the way information is communicated among people. For example, fires, are among major emergencies that can hinder or paralyze the communication equipment within their areas putting human life in danger, and one potential consequence coming from communication infrastructure damage is the inability to communicate important information such as fireman location, survival number estimations, victims’ identifications, or rescue operations. All this information may directly affect the decision making of the emergency management team. Innovative Location Based Critical Applications (LBCAs) are demanding and finding their impacts in almost in every aspect of disasters and emergencies details. Such critical applications are providing services in various categories including tracking people, navigation to emergency exits, locations of healthcare aid equipment’s etc. …, such services are dynamic and optimized based on their users’ geographical location either inside and/or outside the buildings. By using the already deployed University of Dubai IT infrastructure along with the university occupants’ smartphones, the goal of the proposed project is to determine and track in real time the location of smartphone users inside the university building. Moreover, developing an Android application for indoor tracking and navigation purposes that could act as an enabler technology to save human life, time, and efforts of the university inhabitants either for locating their current position on campus map or to guide them to reach their emergency exits. However, the resulted application could be used for tracking the student’s mobility pattern in order to better distribute the offered services such as Wi-Fi access also to spot the dense and congested areas inside the university campus. In this research, we aim to develop a mobile based system that will be able to determine people location within few meters accuracy (10 meters accuracy satisfies the requirements of the aforementioned Critical Applications) inside buildings-based Wi-Fi Received Signal Strength Indicator (RSSI) measurements with zero infrastructure. To the best of our knowledge, the proposed system is the first of its kind to integrate simulated radio map with indoor localization techniques using different mathematical models and machine learning (ML) approaches to accurately compute people locations. Received Signal Strength Indicator (RSSI)-based fingerprinting is a highly appreciated indoor positioning method due to its efficiency. However, it lacks the efficiency in term of time to generate an RSSI radio map manually. In this proposal, radio maps are generated based on realistic simulation method instead of the currently used method collecting an actual-experimental RSSI. Wireless-InSite simulator-based method which is used to generate a fully simulated radio are studied to reduce time for radio map generation. Location estimation process is executed depends on the prerecorded radio maps and on a dedicated mobile application. The proposed localization method will be ported into a mobile critical based application. The proposed system requires zero-configuration because the off-line calibration of the effect of wireless physical characteristics on RSSI measurement is simulated and no on-site survey or initial training is required to bootstrap the system.
