The First International Workshop on

Data Science for Internet of Things

Brasilia, Brazil
Co-located with MASS 2016

Technical Program

08:30 - 08:45

Welcome presentation
Gabriel Martins Dias (Universitat Pompeu Fabra)

08:45 - 09:15

A Review on Big Data Analysis and Internet of Things
Umar Ahsan (University of Regina), Abdul Bais (University of Regina)

09:15 - 09:45

Toward Anonymizing IoT Data Streams via Partitioning
Ankhbayar Otgonbayar (University of the West of Scotland), Zeeshan Pervez (University of the West of Scotland), Keshav Dahal (University of the West of Scotland)

09:45 - 10:15

Data Provenance in Environmental Monitoring
Daniel da Silva (University of São Paulo, Brazil), Andre Batista (University of São Paulo, Brazil), Pedro Luiz Pizzigatti Corrêa (University of São Paulo, Brazil)

10:15 - 10:30


10:30 - 11:00

A Testbed for Security and Privacy Analysis of IoT Devices
Ali Tekeoglu (University of Texas at San Antonio), Ali Saman Tosun (University of Texas at San Antonio)

11:00 - 11:30

Visual Analytics improving Data Understandability in IoT Projects: An Overview of the U.S. DOE ARM Program Data Science Tools
Andre Batista (University of São Paulo, Brazil), Pedro Luiz Pizzigatti Corrêa (University of São Paulo, Brazil), Giri Palanisamy (Oak Ridge National Laboratory)

11:30 - 12:00

Sensing And Actuation in IoT: an Autonomous Rule Based Approach
Anderson Cardozo (Universidade Católica de Pelotas), Adenauer Yamin (Universidade Federal de Pelotas), Patricia Davet (Universidade Federal do Rio Grande do Sul), Rodrigo Souza (Universidade Federal do Rio Grande do Sul), João Ladislau Lopes (Universidade Federal do Rio Grande do Sul), Claudio Geyer (Universidade Federal do Rio Grande do Sul)

Important dates

Paper submission

30 June 2016

Acceptance Notification

15 July 2016

Camera Ready Submission

20 July 2016


10 October 2016


Data science is an interdisciplinary field that involves techniques to acquire, store, analyze, manage and publish data. For example, data can be analyzed using machine learning, data analysis and statistics, optimizing processes and maximizing their power in larger scenarios.

In the Internet of Things (IoT), smartphones and household appliances can easily become sensor nodes and compose sensor networks, measuring environmental parameters and generating user interaction data. As sensor networks are mainly data-oriented networks, i.e., sensed data is their most valuable asset and the reason for the operation of the whole network, data science techniques have been adopted to improve the IoT in terms of data throughput, self-optimization and self-management. In fact, incorporating the lifecycle proposed by the data scientists will impact the future of the IoT, allowing researchers to reproduce scenarios, and optimize the acquisition, analysis and visualization of the data acquired by IoT devices.

This workshop will address techniques used for data management planning into IoT scenarios in order to optimize data acquisition, management and later discovery.


Data collection

  • Prediction-based data reduction in the IoT
  • Data-based sensor failure techniques
  • Data-based error detection techniques

Data analysis

  • Methods for assessing IoT data quality
  • Strategies for IoT data visualization

Data management solutions

  • Standards for IoT data discovery
  • IoT data publication
  • Integrating IoT data with external data sources
  • Privacy and security in the IoT data sharing

Reproducibility of IoT scenarios

  • Long-lived IoT data storage
  • IoT data integrity standards
  • IoT data discovery standards
  • IoT data description (metadata)
  • Data-centric simulations of the IoT
  • Tests reproducibility for IoT scenarios

Autonomous IoT

  • Autonomic architectures for IoT
  • IoT-optimization using external information
  • Management of IoT devices based on data knowledge

Data management planning use cases

  • Data science in smart cities
  • Data science in smart environments
  • Data science for wearable devices

Instructions For Authors

Paper submission

Papers must be submitted via EDAS in the following link:

Submitted papers should be written in the English language, with a maximum page limit of 6 printed pages, including all the figures, references and appendices, , and not published or under review elsewhere. Papers longer than 6 pages will not be reviewed. Use the standard IEEE Conference templates for Microsoft Word or LaTeX formats found at:

If the paper is typeset in LaTeX:

  • Please use an unmodified version of the LaTeX template IEEEtran.cls version 1.8, and use the preamble: \documentclass[10pt, conference, letterpaper]{IEEEtran}.
  • Do not use additional LaTeX commands or packages to override and change the default typesetting choices in the template, including line spacing, font sizes, margins, space between the columns, and font types. This implies that the manuscript must use 10 point Times font, two-column formatting, as well as all default margins and line spacing requirements as dictated by the original version of IEEEtran.cls version 1.8.

If you are using Microsoft Word to format your paper:

  • You should use an unmodified version of the Microsoft Word IEEE Transactions template (US letter size).
  • Regardless of the source of your paper formatting, you must submit your paper in the Adobe PDF format. The paper must print clearly and legibly, including all the figures, on standard black-and-white printers. Reviewers are not required to read your paper in color.

    More information and template downloads can be found at the IEEE MASS main page.

    Paper presentation

    At least one author of each accepted paper must register for the workshop and present the paper.

    IEEExplore proceedings

    All DS-IoT 2016 presented papers will be published in the conference proceedings and submitted to IEEE Xplore.

    Best contributions

    Authors of the best technical contributions will be invited to submit an extended version of their paper to EAI Endorsed Transactions on Internet of Things.

Program Committee

  • Ruizhi Liao (Oxford Brookes University, Great Britain)
  • Cristina Cano (INRIA, France)
  • Alessandro Checco (University of Sheffield, Great Britain)
  • Antonio Loureiro (Universidade Federal de Minas Gerais, Brazil)
  • Elena Gaura (Coventry University, Great Britain)
  • Luiz Fernando Bittencourt (Universidade Estadual de Campinas, Brazil)
  • Liansheng Tan (Central China Normal University, China)
  • Juan José Murillo Fuentes (Universidad de Sevilla, Spain)
  • Amy Lynn Murphy (Bruno Kessler Foundation, Italy)
  • Vanesa Daza (Universitat Pompeu Fabra, Spain)

General Chairs


The 13th IEEE International Conference on Mobile Ad hoc and Sensor Systems (IEEE MASS 2016) will be held in Brasilia, Brazil, October 10-13, 2016.

Wireless ad hoc communication has applications in a variety of environments, such as conferences, hospitals, battlefields, and disaster-recovery/rescue operations, and is also being actively investigated as an alternative paradigm for Internet connectivity in both urban and rural areas. Wireless sensor and actuator networks are also being deployed for enhancing industrial control processes and supply-chains, and for various forms of environmental monitoring. IEEE MASS 2016 aims at addressing advances in research on multihop ad hoc and sensor networks, covering topics ranging from technology issues to applications and test-bed development.

IEEE MASS 2016 website

"María de Maeztu" Unit of Excellence

The Department of Information and Communication Technologies at Universitat Pompeu Fabra is a "María de Maeztu" Unit of Excellence from the Ministry of Economy and Competitiveness.