Data Fusion in the Internet of Things 2017 (DFIOT 2017)

The recent technological advances in computer and communication technologies have been fostering an enormous growth in the number of smart objects available for usage. The integration of these smart objects into the Internet originated the concept of Internet of Things (IoT). The IoT vision advocates a world of interconnected objects, capable of being identified, addressed, controlled, and accessed via the Internet. Such objects can communicate with each other, with other virtual resources available on the web, with information systems and human users. IoT applications involve interactions among several heterogeneous devices, most of them directly interacting with their physical surroundings.

New challenges emerge in this scenario as well as several opportunities to be exploited. One of such opportunities regards the leveraging of the massive amount of data produced by the widely-spread sensors to produce value-added information for the end users. In this context, techniques to promote knowledge discovery from the huge amount of sensing data are required to fully exploit the potential usage of the IoT devices. In this context, data fusion techniques are data techniques dealing with the association, correlation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and threats, and their significance. Since IoT data is usually dynamic and heterogeneous, it becomes important to investigate techniques for understanding and resolving issues about data fusion in IoT. Employment of such Data fusion techniques are useful to reveal trends in the sampled data, uncover new patterns of monitored variables, make predictions, thus improving decision making process, reducing decisions response times, and enabling more intelligent and immediate situation awareness.

The goal of this Workshop is to present and discuss the recent advances in the interdisciplinary data fusion research areas applied to IoT. We aim to bring together specialists from academia and industry in different fields to discuss further developments and trends in data fusion area. This workshop will be held in conjunction to PICOM 2017 – http://cse.stfx.ca/~picom2017.

Topics appropriate for this workshop include (but are not necessarily limited to):

  • Data collection and abstraction in IoT
  • Knowledge fusion in IoT
  • Machine learning, data mining and fusion for IoT
  • Data streams fusion in IoT
  • Data models for IoT
  • Fusion models for IoT
  • Subjective Logic
  • Dynamic analysis in IoT
  • Social data fusion and social IoT
  • Probabilistic reasoning in IoT
  • Decision systems in IoT
  • Web data fusion
  • Image Fusion
  • Tracking

Organizing Committee:

  • Claudio M. de Farias – Federal University of Rio de Janeiro
  • Flávia C. Delicato – Federal University of Rio de Janeiro
  • Luci Pirmez – Federal University of Rio de Janeiro