A data fusion framework for large-scale measurement platforms

The need to assess internet performance from the user's perspective grows, as does the interest in deployment of Large-Scale Measurement Platforms (LMAPs). The potential of these platforms as a real-time network diagnostic tool is limited by the volume, velocity and variety of the data they generated. Fusing this data from multiple sources and generating a single piece of coherent information about the state of the network would increase the efficiency of network monitoring. The current practice of visually analysing LMAPs' data stream would certainly benefit from having automatically generated notifications in a timely manner alerting human controllers to the network's conditions of interest. This paper proposed a data fusion framework for LMAPs that makes use of mathematical distribution based sensors to generate probabilistic sensor outputs which are fused using a Dempster-Shafer Theory.