Real-Time Data Services for the Internet of Things
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 448
Special Issue Editors
Interests: Internet of Things; cyber-physical systems; real-time embedded systems
Special Issue Information
Dear Colleagues,
The Internet of Things (IoT) interconnects embedded sensors, actuators, robots, vehicles, homes, buildings, factories, electric grids, and humans to support numerous important applications, including cognitive assistance, health care, smart homes and buildings, intelligent transportation, smart grids, and industry 4.0, with tremendous societal and economic impacts. By 2020, there will be tens of billions of connected things expected to generate trillion dollar revenues according to Cisco and Gartner. To realize the IoT vision, it is required to collect, exchange, store, analyze, and visualize high volume of data in real-time to detect, predict, and react to important events, such as abnormal health conditions of vulnerable populations, traffic delays and incidents, road hazards, demand-supply analysis and prediction for electric grids, and malfunctioning industrial robots, in a timely fashion. Achieving this goal, however, is challenging due to stringent timing constraints and big/fast real-time data streams. Enabling technologies to be explored include novel networking infrastructures and protocols, real-time data stream management and analytics (in things, within networks, at network edges, and in cloud), consumer and industry case studies, and security/privacy, which can handle and support IoT heterogeneity, scalability, and interoperability. This Special Issue calls for original research articles, case studies, and applications that advance technologies for real-time data services for IoT.
Prof. Dr. Kyoung-Don Kang
Prof. Dr. Song Han
Guest Editors
Manuscript Submission Information
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Keywords
- Real-time networking for IoT
- Edge or Cloud Computing for Real-Time Data Analysis
- Scalable and interoperable real-time middleware for IoT
- Real-time data processing systems for stream data management, complex event processing, etc.
- Real-time data analytics via online machine learning
- Time series analysis for stream data
- Context analysis and event detection
- Data cleaning, alignment, quality management, and visualization
- Security and privacy
- Emerging applications and case studies
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