Centaur

We consider the problem of locating devices such as laptops, desktops, smartphones etc. within an office environment, without requiring any special hardware or infrastructure. We consider two widely-studied approaches to indoor localization: (a) those based on Radio Frequency (RF) measurements made by devices with WiFi or cellular interfaces, and (b) those based on Acoustic Ranging (AR) measurements made by devices equipped with a speaker and a microphone. A typical office environment today comprises devices that are amenable to either one or both these approaches to localization. In this paper we ask the question, "How can we combine RF and AR based approaches in synergy to locate a wide range of devices, leveraging the benefits of both approaches?" The key contribution of this paper is Centaur, a system that fuses RF and AR based localization techniques into a single systematic framework that is based on Bayesian inference. Centaur is agnostic to the specific RF or AR technique used, giving users the flexibility of choosing their preferred RF or AR schemes. We also make two additional contributions: making AR more robust in non-line-of-sight settings (EchoBeep) and adapting AR to localize speaker-only devices (DeafBeep). We evaluate the performance of our AR enhancements and that of the Centaur framework through microbenchmarks and deployment in an office environment.