A Data Fusion Framework for Large-Scale Measurement Platforms

Date

2015-12-28

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

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.

Description

Keywords

data fusion, sensors, active measurements, largescale measurement platform

Citation

Rattadilok, P. et al. (2015) A data fusion framework for large-scale measurement platforms. Big Data (Big Data), 2015 IEEE International Conference on,

Rights

Research Institute