posted by user: yongrui || 1204 views || tracked by 3 users: [display]

WCMC SI BDAFC 2018 : Big IoT Data Analytics in Fog Computing

FacebookTwitterLinkedInGoogle

Link: https://www.hindawi.com/journals/wcmc/si/716105/cfp/
 
When N/A
Where N/A
Submission Deadline Nov 3, 2017
Final Version Due Mar 31, 2018
Categories    computer science   internet of things   big data   fog computing
 

Call For Papers

Wireless Communications and Mobile Computing (WCMC) Speical Issue: Big IoT Data Analytics in Fog Computing


The number of devices within Internet of Things (IoT) that are connected and available via Internet will be between 50 and 100 billion by 2020. The IoT devices are typically the sensors embedded in environments, buildings, vehicles, manufacturing processes, and products or attached to the people. The amount of the data generated by IoT devices grows exponentially as these devices operate nonstop, 24/7, creating an avalanche of data that is out of the control of existing and foreseeable data processing and analytics techniques. On the other hand, we can create numerous opportunities to extract unprecedented insightful information. Unlocking the value of big data through analytics and mining has been regarded as the key enabler of many innovation and marketing strategies which, in turn, has pushed more efforts and supports to the IoT and big data related R&D. While data processing is typically envisaged to be conducted in clouds, it alone is suffering from growing limitations in meeting demands of numerous applications where the local computation nearby data sources is required for low-latency response, contextual information integration, or networking load reduction. Meanwhile, moving all the data generated from IoT devices into cloud server farms for further processing or storage poses overwhelming challenges on the Internet infrastructure and is often prohibitively expensive, technically impractical, and mostly unnecessary.

Fog computing is an emerging paradigm based on creation of micro clouds (called fog nodes) near the sources of data. It is a promising approach to processing data before they even attempt to reach cloud, shortening the communication times and cost, as well as reducing the need for huge data storage. It seamlessly bridges IoT devices and the remote cloud data centers by pushing cloud computing, storage, and networking services down closer to end IoT devices. Fog computing has seen a rapidly increasing number of applications in many industries such as manufacturing, e-health, oil and gas, smart cities, smart homes, and smart grids. However, it is still in its early stages and presents a set of new challenges with the increasing adoption of this computing paradigm, such as fog architecture, frameworks and standards, computing, storage and networking resource provisioning and scheduling, programming abstracts and models, and security and privacy issues. In particular, big IoT data analytics with fog computing infrastructure is in its nascent stage but of paramount importance and requires extensive research in order to conduct more efficient knowledge discovery and smart decision support.

Many relevant theoretical and technical issues have not been answered well yet, for example, how to abstract programming interfaces of fog infrastructure and platforms for data analytics, how to design scalable data mining algorithms with the use of fog infrastructure, how to achieve secure and privacy-preserving data analytics in fog computing. As such, it is high time that the related issues in big IoT data analytics with fog infrastructure are investigate by examining fog architecture, platforms, and applications in detail, hence the call for this special issue.

Potential topics include but are not limited to the following:

Fog architectures, frameworks, standards, and platforms for IoT data analytics
Fog programming abstracts, models, and toolkits for data analytics
Wireless communication supports for fog computing
Mobile computing with the support of fog computing
Load balancing and resource scheduling and management in fog computing
Middleware for distributed data management in fog computing
Data mining and machine learning algorithm design in fog computing
Theory and modelling of distributed intelligence in fog computing for IoT data analytics
Multisource and heterogeneous IoT data analytics with fog
Time-critical and low-latency data analytics with fog
Spatial and temporal data processing and analytics in fog computing
Fog data analytics applications, for example, smart cities, e-health, and smart homes
Information retrieval design and knowledge assistance for fog computing data analytics
Context-aware IoT applications in the fog
Disaster and emergency management in IoT with fog
Recovery schemes in case of fog down
Pricing models for IoT data analytics in fog computing
Privacy and security issues related to fog data analytics
Authors can submit their manuscripts through the Manuscript Tracking System at https://mts.hindawi.com/submit/journals/wcmc/bdafc/.

Submission Deadline Friday, 3 November 2017
Publication Date March 2018
Papers are published upon acceptance, regardless of the Special Issue publication date.

Lead Guest Editor

Xuyun Zhang, University of Auckland, Auckland, New Zealand

Guest Editors

Yongrui Qin, University of Huddersfield, West Yorkshire, UK
Deepak Puthal, University of Technology Sydney, Ultimo, Australia
Xiaobing Wu, University of Canterbury, Christchurch, New Zealand

Related Resources

ADAH 2017   Advanced Data Analytics in Health
ICDM 2018   18th Industrial Conference on Data Mining ICDM 2018
IPMU 2018   17th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference
SI Wiley SPE 2018   Call for Papers for Special Issue on Integration of Cloud, IoT and Big Data Analytics
ICS 2018   the 32nd ACM International Conference on Supercomputing
UMUAI SI 2018   UMUAI SI on Multimodal Learning Analytics & Personalized Support Across Spaces
CAiSE 2018   CfP: CAiSE 2018 (30th International Conference on Advanced Information Systems Engineering)
Multimedia Analysis for IoT 2018   Multimedia Analysis for Internet-of-Things
Cluster-BigData 2018   Call for Springer book Chapters: Clustering methods for Big Data Analytics: techniques, toolboxes and applications, Springer (USA)
IEEE - ICBDA 2018   2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA 2018)--IEEE Xplore and Ei Compendex