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.