SBIR Phase II: Mobile Indoor Localization and Navigation System Using Sensory Data with Data Mining and Machine Learning Techniques

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will result from a revolution in the way buildings are used. If successfully implemented, the technology offers a solution to both end-users and companies. Users inside buildings will have access to floorplans, points of interest, location-based information, and turn-by-turn directions directly on their smartphones. Companies will be able to offer new experiences to customers, analyze their movements, and provide them with targeted information or advertisements when and where they need them. Other applications of the technology will provide considerable societal benefits: (i) first responders will be able to accurately localize victims, thus reducing response times and saving lives; (ii) building managers will be able to condition rooms in real-time based on their occupancies, substantially reducing energy consumption; (iii) people with disabilities will be able to obtain assistance by finding wheelchair-accessible routes; (iv) warehouse managers will be able to reduce order fulfillment time; (v) service providers (e.g., hospitals, military, IT) will be able to track, dispatch, and more efficiently manage critical workforce personnel (e.g. doctors and technicians). Indoor localization is expected to be, in the near future, as pervasive as GPS is today. This Small Business Innovation Research (SBIR) Phase II project will further develop the company's indoor localization technology and deploy it to mobile devices. By analyzing and processing smartphones' accelerometer, cellular, magnetometer, orientation, and WiFi sensor data, a building's sensory blueprint can be created. The building's sensory blueprint can then be exploited to localize people holding smartphones, by means of a combination of machine learning, data mining, sensor fusion, and statistical, tracking, and path planning algorithms. The project aims to develop and implement the following software services: (i) a mapping service that converts a smartphone's sensor readings into a sensory blueprint; (ii) a localization service that allows end-users to view their location inside buildings on their smart mobile devices; (iii) a navigation service that provides paths and turn-by-turn directions to points of interest; (iv) a location-based service that presents interesting information in the user's vicinity; (v) a behavior analytics engine that displays statistical information about a user's movement or building's utilization; (vi) a software package that facilitates the technology's distribution. These services will be deployed within a server-client framework, alleviating the space, memory, and computational constraints imposed by mobile devices.