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Introduction

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 a number of 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 Special Section 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.

Call for paper

Important date

2017-01-31
Draft paper submission deadline
2017-02-28
Draft paper acceptance notification
2017-03-15
Final paper submission deadline

Submission Topics

Topics appropriate for this special issue 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

  • Probabilistic reasoning in IoT

  • Decision systems in IoT

  • Web data fusion

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Important Date
  • Conference Date

    May 16

    2017

    to

    May 18

    2017

  • Jan 31 2017

    Draft paper submission deadline

  • Feb 28 2017

    Draft Paper Acceptance Notification

  • Mar 15 2017

    Final Paper Deadline

  • May 18 2017

    Registration deadline

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