Gabriel Martins Dias (Universitat Pompeu Fabra)
A Review on Big Data Analysis and Internet of Things
Umar Ahsan (University of Regina), Abdul Bais (University of Regina)
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)
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)
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)
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)
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)
30 June 2016
15 July 2016
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.
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.
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.