WiFinder

Description

In a world that is rapidly expanding, interconnecting, and growing in complexity, it’s no surprise that navigating it—both physically and conceptually—has become increasingly challenging. The physical environments we inhabit now reflect this societal shift toward intricacy and scale, making tools that assist in wayfinding more essential than ever. Our team, Big Data, was tasked not only with the programming and technical development of a solution, but also with pioneering a tailored pipeline to support future researchers at the University of Auckland in their efforts to iterate on, enhance, and advance the boundaries of indoor navigation research. Enter WiFinder – a purpose-built tool that aims to simplify the often frustrating task of navigating complex indoor environments. Harnessing technologies such as WiFi signal analysis, motion detection, particle filtering, and machine learning, WiFinder can accurately detect and track a user’s location and movement within a building. The current version of the app supports a limited area within the Science Building (Building 302), specifically on Floors G, 1, and 2, with a small selection of rooms available for navigation. To support ongoing development, the application includes a suite of debugging tools that provide clearer insight into its core functions. Alongside the tool, our comprehensive report outlines key findings and offers multiple avenues for future improvement and exploration.