DFIoT 2020 – Data Fusion in the Internet of Thigs

Co-located with IEEE 18th International Conference on Pervasive Intelligence and Computing – PICom 2020 (http://cyber-science.org/2020/picom)

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. 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 deal 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, as well as their significance. Among the techniques that have been exploited for data fusion, we can point out Machine Learning, AI, Filters, Probabilistic Reasoning, to name a few. Since IoT data is usually dynamic and heterogeneous, it becomes important to investigate techniques for understanding and resolving issues about data fusion in the specific context of 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 the data fusion area.

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 applied to IoT
  • Dynamic analysis in IoT
  • Social data fusion and social IoT
  • Big Data Fusion in Internet of Things  
  • Probabilistic reasoning in IoT
  • Web data fusion
  • Image Fusion
  • Tracking algorithms for mobile IoT
  • Artificial Neural Networks for IoT
  • AI on Edge
  • Distributed data fusion for IoT
  • TinyML

The submission dates are:

  • Submission Due: June 02, 2020 
  • Author Notification: June 20, 2020
  • Camera-ready Paper Due: July 31, 2020

Authors are invited to submit their original research work that has not previously been published or under review in any other venue. Authors should submit (6 pages) papers using IEEE template (https://www.ieee.org/conferences_events/conferences/publishing/templates.html).  Papers should be submitted via EDAS systems. At least one of the authors of any accepted paper is requested to register and present the paper at the conference.

Program Committee 

Raffaele Gravina – University of Calabria, Italy
Giancarlo Fortino – University of Calabria, Italy
Joel Reijonen -Ericsson Research, Jorvas, Finland
Klimis Ntalianis – University of West Attica, Greece
Eduardo Hargreaves – Petrobrás S.A., Brazil
Priscila Machado Vieira Lima – Federal University of Rio de Janeiro, Brazil
Miodrag Bolic – University of Ottawa, Canada
Antonio Guerrieri – ICAR-CNR, Italy
Alan Oliveira – Navy War School, Brazil
Tiago França – Federal Rural University of Rio de Janeiro, Brazil
Igor Leão dos Santos – CEFET-RJ, Brazil
Leandro Santiago – Fluminense Federal University, Brazil
Edgar Ramos – Ericsson Research, Jorvas, Finland
José Brancalion – EMBRAER S.A., Brazil
Roberto Morabito – Ericsson Research, Jorvas, Finland
Hiroshi Doyu – Ericsson Research, Jorvas, Finland

For submission: edas.info/newPaper.php?c=26908&track=100236

For any question, please don’t hesitate to contact the organization at cmicelifarias at gmail dot com.