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Workshop on Data Fusion in the Internet of Things (DFIoT)
Co-located with IEEE 15th International Conference on Pervasive
Intelligence and
Computing – PICom 2017 (http://cse.stfx.ca/~picom2017/)
Orlando, USA
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CALL FOR PAPERS
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 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
• Probabilistic reasoning in IoT
• Decision systems in IoT
• Web data fusion
• Image Fusion
• Tracking
The submission dates are:
• Submission Due: 10 August 2017
• Author Notification: 24 August 2017
• Camera-ready Paper Due: 1 September 2017
Authors are invited to submit their original research work that has not
previously been published or under review in any other venue. Papers should
be prepared in IEEE CS Proceedings format and submitted via EDAS systems.
Research papers should explore a specific technology problem and propose a
complete solution to it. Authors should submit 6 pages Research papers
using IEEE template. At least one of the authors of any accepted paper is
requested to register and present the paper at the conference.
Organizing Committee:
• Claudio M. de Farias – Federal University of Rio de Janeiro - Brazil
• Flávia C. Delicato – Federal University of Rio de Janeiro – Brazil
• Luci Pirmez – Federal University of Rio de Janeiro - Brazil
Program Committee:
• Atslands Rego – Federal University of Ceará – Brazil
• Danielo Gomes – Federal University of Ceará – Brazil
• Kevin Wang – The University of Auckland – New Zealand
• Rodrigo Pereira David – INMETRO – Brazil
• Antonio Balzanella – Dipartimento di Matematica e Fisica, Seconda
Universita degli
Studi di Napoli – Italy
• Celio Albuquerque – Fluminense Federal University – Brazil
• Haibo Zhang – University of Otago – New Zealand
• Paulo Pires – Federal University of Rio de Janeiro – Brazil
• Raquel A. F. Mini – PUC-Minas – Brazil
• Andre Aquino – Federal University of Alagoas – Brazil
• Wei Li – University of Sydney – Australia
• Antonio Guerrieri – ICAR – Italy
• Reyes Juarez Ramirez – University of Baja California – Mexico
• Jose Brancalion – Embraer – Brazil
• Andrea Omicini – University of Bolonha – Italy
• Flavio Mello – Federal University of Rio de Janeiro – Brazil
• Jonice Oliveira – Federal University of Rio de Janeiro – Brazil
• Jó Ueyama – University of São Paulo – Brazil
• Haibin Zhu – Nipissing University – Canada
• Raffaele Gravina – University of Calabria – Italy
• Arnoldo Diaz Ramirez – Instituto Tecnologico de Mexicali – Mexico
• Joni Amorim – University of Campinas – Brazil
• José Neuman – Federal University of Ceará – Brazil
• Alessandra De Paola – Università degli Studi di Palermo – Italy
• Florin Pop – University Politehnica of Bucharest / ICI Bucharest – Romenia
• Belur Dasarathy – Independent Consultant – USA
• Giancarlo Fortino – University of Calabria – Italy
• Priscila Machado Vieira Lima – Federal University of Rio de Janeiro –
Brazil
• Audun Josang – University of Oslo – Norway
• Francisco Herrera – University of Granada – Spain
• Adriana Vivácqua – Federal University of Rio de Janeiro – Brazil
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Alessandra De Paola - Ph.D.
DIID - Dipartimento dell'Innovazione Industriale e Digitale
Università degli Studi di Palermo
Viale delle Scienze, Edificio 6, 3° piano
90128 Palermo
phone: +39 091 238 62604
e-mail: alessandra.depaola@unipa.it
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