Infrastructure for benchmarking RF-based indoor localization under controlled interference

The proliferation of RF-based indoor localization solutions raises the need for testing systems that enable objective evaluation of their functional and non functional properties. We introduce a testbed and cloud infrastructure for supporting automatized benchmarking of RF-based indoor localization solutions under controlled interference. For evaluating the impact of RF interference on the performance of benchmarked solution, the infrastructure leverages various interference generation and monitoring devices. The infrastructure obtains location estimates from the System Under Test (SUT) using a well defined interface, and the estimates are subsequently processed in a dedicated metrics computation engine and stored in the dedicated engine for storing the results of benchmarking experiments. The infrastructure further includes a robotic mobility platform which serves as a reference localization system and can transport the localized device of the evaluated indoor localization solution in an autonomous and repeatable manner. We present the accuracy of our autonomous mobility platform in two different setups, showing that, due to the high accuracy, the location estimation provided by the platform can be considered as the reference localization system for benchmarking of RF-based indoor localization solutions. The results, as well as the raw data from the benchmarking experiments, can be stored into the dedicated publicly available services which gives the opportunity of reusing the same data for benchmarking different solutions. Finally, we present the capabilities of the testbed and cloud infrastructure on the use-case of benchmarking of an example WiFi fingerprinting-based indoor localization solution in four different interference scenarios.