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ibdaa 2020 : Workshop on IoT based Big Data Architectures and Applications - in conjunction with IEEE Big Data 2020


When Dec 10, 2020 - Dec 13, 2020
Where Atlanta, Georgia
Submission Deadline Sep 15, 2020
Notification Due Oct 20, 2020
Final Version Due Nov 15, 2020
Categories    IOT   big data   internet of things   data analytics

Call For Papers

Due to the increased growth in data analytics and the proliferation of applications accelerating the ubiquity of digital information, the concept of the Internet of Things (IoT) has been materialized. The IoT is representative of an environment comprising of a connected set of individuals, things, or objects that can communicate through the existing network infrastructure and result in improved efficiency, accuracy, and economic benefit. Considering the demand to efficiently process diverse types of big data being generated at a rapid pace by a multitude of devices, the need to develop efficient models has increase manifold. Therefore, the workshop titled “Workshop on IoT based Big Data Architectures and Applications” co-located with the IEEE International Conference on Big Data 2020 encourages the researchers around the world to submit their contributions pertaining to the IoT based architectures and their applications for processing the big data.

-Topics of interest include but are not limited to:
Big data storage, distribution, and management
Big data applications and models for e-Governance
Big data analytics, streaming, and processing in fog environments
Industrial big data analytics and Internet of Things (IoT)
Machine learning models for the Internet of Things (IoT) on Edge devices
Connected Health and IoT
Scalable and context-aware remote health monitoring services
Leveraging the IoT and big data architectures for smart homes, smart buildings, and smart cities
Trust, Security, and privacy in IoT
Deep learning and big data applications for the finance industry
IoT and Precision Agriculture
Edge computing for smart cities and urban surveillance
IoT and Cyber-Physical Systems
Resource allocation and interoperability in IoT
Experimental evaluation of data-intensive frameworks

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