Challenges on Managing and Analyzing Big Data from Internet of Things Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 188
Special Issue Editors
Interests: Internet of Things (IoT); massive data collection and management; data analytics; health monitoring; wearable sensors; edge/fog computing; block chain; storage systems
Interests: computational and data-enabled science and engineering; parallel and distributed computing (HPC, cloud and grid computing); extreme-scale computing and data management; autonomic computing
Interests: edge computing; data centers; parallel and distributed computing; scheduling; sustainable computing
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Special Issue Information
Dear Colleagues,
The billions of sensors of many types (wearable, cars, industry 4.0, cities, etc.) connected to the Internet of Things (IoT) generate massive amounts of heterogeneous data in real-time. The existence of all those data generates new business and research opportunities in many areas, assuming that they can be collected, stored, processed, and analyzed to get the information hidden in the data. However, due to the volume, variety, and velocity of data, there are still many challenges associated with Big Data and IoT, such as data sensors processing at high speed, data interoperability, resource allocation of resources to process data in the expanded landscape (fog, edge, cloud), orchestration for collaborative computing, quality of service, data security and privacy, data analysis facilities in fog/edge/cloud, learning tools, etc.
The goal of this Special Issue is to provide a forum for researchers working on IoT and fog/edge and cloud computing to present their recent research results in collecting, managing, and analyzing data from IoT sensors. We are looking for high-quality papers related to several aspects of the area, from technology, to systems software and real-world applications.
Prof. Dr. Jesus Carretero
Prof. Dr. Manish Parashar
Prof. Dr. Albert Zomaya
Guest Editors
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Keywords
- Artificial Intelligence (AI) and machine-Learning methods for stream data analytics
- Big-data analytics, machine learning algorithms, and scalable/ parallel/ distributed algorithms for Internet of Things
- Fog/edge analytics of Internet of Things data
- Novel mechanisms for managing big data into Internet of Things environments
- New tools for improving the communication of the Internet of Things and the cloud
- Data management from Internet of Things streams, including real-time constraints
- Models for edge–cloud integration
- Programming systems for edge systems and cloud–edge integration
- Autonomic runtimes for edge/cloud–edge environments
- End-to-end application workflows
- Secure middleware for Internet of Things data collection and transmission
- Quality of service assurance for Internet of Things, sensors data
- Data privacy and security in the Internet of Things, including usage of blockchain, to manage sensors data for large-scale analytics
- Self-monitoring, self-diagnosing, self-healing, and self-directing IoT systems
- Novel devices and circuits and architectural support for big data and Internet of Things
- Use case application joining dig data and sensors networks (wearable sensors, industry 4.0, autonomous driving vehicles, etc.)
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