Accurate wireless measurements are not only critical to the sustained growth of deployed wireless networks, but also the successful proposal and development of innovative technologies to address their shortcomings. Because of the significant effort required to deploy wireless networks and obtain reliable and repeatable measurements, however, currently available wireless traces are severely lacking in both breadth of environments and consistency in methodology.

While significant progress has been made in improving the availability of wireless traces, such as, for instance, through the CRAWDAD data repository, the usability of these data sets is still

fundamentally limited by the lack of consistency across traces in both measurement parameters and methodology.  For researchers wanting to confirm the validity of an observation based on local measurements, it is extremely difficult or impossible for them to locate another measurement, collected in a similar way on a similar network. Even if they manage to locate such a trace, it is likely that the desired metric of interest is missing from the second trace, thereby preventing any meaningful cross-comparison of network deployments. As a result, researchers must make observations and design network solutions based on datasets with limited scope.  This makes the broad applicability of these solutions difficult to confirm.

To facilitate meaningful analysis of wireless networks, we need a way to collect measurement traces across a wide variety of network deployments,  using a consistent set of measurement metrics. Distributed multi-faceted data collection will provide multiple viewpoints for the same network, while consistent metrics across environments and datasets will increase confidence

and reliability of research observations, and provide opportunities for cross-correlation of observed network phenomena.