Many indoor localization algorithms have been proposed to enable location-based applications in indoor environments. However, these systems are monolithic and not componentbased. We present BearLoc, a distributed modular framework for indoor localization systems that provides (1) natural development abstractions for sensor, algorithm, and application components, and (2) easy and flexible component composition. We demonstrate the merits of BearLoc with an example use case. Our evaluation shows we can reduce developer lines of code by 60% while introducing acceptable network delay overhead. [github]
BearLoc: A Composable Distributed Framework for Indoor Localization Systems
Kaifei Chen, Siyuan He, Beidi Chen, John Kolb, Randy H. Katz, and David E. Culler. International Workshop on IoT Challenges in Mobile and Industrial Systems (IoT-Sys), May 2015. [pdf]
This material is based upon work supported by the National Science Foundation under grants CPS-1239552, and the NSF Graduate Research Fellowship Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.