Call for papers:

IEEE Transactions on Emerging Topics in Computing

Special Issue on "Methods and Techniques for Processing Streaming Big Data in Datacentre Clouds"

IMPORTANT DATES

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Submission Deadline: June 1 2015  

Reviews Completed: September 1 2015  

Major Revisions Due (if Needed): October 1 2015 

Reviews of Revisions Completed (if Needed): November 1 2015  

Minor Revisions Due (if Needed): December 1 2015  

Notification of Final Acceptance: February 1 2016 

Publication Materials for Final Manuscripts Due: March 1 2016  

Publication date: Second Issue 2016 (June Issue)

DETAILS ABOUT THE ISSUE

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Internet of Things (IoT) is an emerging paradigm that has gained a significant interest from both academia and industry.  IoT is a part of Future Internet and comprises many billions of Internet connected Objects (ICOs) or ‘things’ where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes.  ICOs can include sensors, RFIDs, social media, actuators (such as machines/equipments fitted with sensors and deployed for mining, oil exploration, and manufacturing operations) as well as lab instruments (e.g., high energy physics synchrotron), and smart consumer appliances (smart TV, smart phone, etc.). This IoT vision has recently given rise to the notion of IoT big data applications that are capable of producing billions of data stream and tens of years of historical data to provide the knowledge required to support timely decision making. Some of the emerging IoT big data applications, e.g. smart energy grids, syndromic bio-surveillance, environmental monitoring, emergency situation awareness, digital agriculture, and smart manufacturing, need to process and manage massive, streaming, and multi-dimensional (from multiple sources) data from geographically distributed data sources.  

Despite recent technological advances of the data-intensive computing paradigms (e.g. the MapReduce paradigm, workflow technologies, stream processing engines, distributed machine learning frameworks) and datacentre clouds, large-scale reliable system-level software for IoT big data applications are yet to become commonplace. As new diverse IoT applications begin to emerge, there is a need for optimized techniques to distribute processing of the streaming data produced by such applications across multiple datacentres that combine multiple, independent, and geographically distributed software and hardware resources. However, the capability of existing data-intensive computing paradigms is limited in many important aspects such as: (i) they can only process data on compute and storage resources within a centralised local area network, e.g., a single cluster within a datacentre. This leads to unsatisfied Quality of Service (QoS) in terms of timeliness of decision making, resource availability, data availability, etc. as application demands increase; (ii) they do not provide mechanisms to seamlessly integrate data spread across multiple distributed heterogeneous data sources (ICOs); (iii) lack support for rapid formulation of intuitive queries over streaming data based on general purpose concepts, vocabularies and data discovery; and (iv) they do not provide any decision making support for selecting optimal data mining and machine algorithms, data application programming frameworks, and NoSQL database systems based on nature of the big data (volume, variety, and velocity). Furthermore, adoption of existing datacentre cloud platform for hosting IoT applications is yet to be realised due to lack of techniques and software frameworks that can guarantee QoS under uncertain big data application behaviours (data arrival rate, number of data sources, decision making urgency, etc.), unpredictable datacentre resource conditions (failures, availability, malfunction, etc.) and capacity demands (bandwidth, memory, storage, and CPU cycles). It is clear that existing data intensive computing paradigms and related datacentre cloud resource provisioning techniques fall short of the IoT big data challenge or do not exist. Topics of interest include, but are not limited to: