Indoor Localization using Accidental Infrastructure

We can foresee a near-future scenario where a huge number of semi-intelligent devices are part of our everyday environment, our homes, the public places and the office as well. The intelligent thermostat uploads the temperature readings to an online database; the fridge sends a tweet when we are out of milk; the coffee machine texts us when the coffee is ready. Each device has a unique and individual purpose. But what if they could be grouped together as a so-called accidental infrastructure to serve a more advanced cause? We have set out to demonstrate the possibilities of such an accidental infrastructure in the field of indoor localization. An ambient device in itself is not intentionally prepared for localization purposes, but using many of them together and combining the collected data can surpass the devices' limited individual capabilities. Our approach was to build a prototype system based on a homogeneous array of radio-connected nodes and an additional entity with a higher magnitude of computing power. This central entity then controls the data collection from the nodes and executes a custom localization algorithm, based on probabilistic methods and a Kalman filter. We have evaluated our system both by simulations with ideal input data and by real-world measurements. The results show that the system is able to track and update the location estimates, but due to the heavy multipath effect it is only capable of very moderate improvements.Embedded SoftwareElectrical Engineering, Mathematics and Computer Scienc