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Real-Time Edge Analytics for Big Data in Internet of Things

Submission Deadline: 15 December 2017

Submission Deadline: 15 December 2017

IEEE Access invites manuscript submissions in the area of Real-Time Edge Analytics for Big Data in Internet of Things.

With the explosive growth in the number of Internet-connected devices, such as smart “things”, traffic sensors, distributed video cameras, and connected appliances, a flood of data is being generated, which is processed using edge computing resources. The rise of Big Data brings extraordinary new benefits and opportunities in several application domains. The relevant enterprises can exploit the Internet of Things (IoT) generated data and infer the business value by performing data analysis. Specifically, some applications would need real-time edge analytics to quickly find useful correlations, customer preferences, hidden patterns, and other valuable information that can assist organizations and decision makers to take more-informed business actions. These challenges represent several opportunities for researchers in the domain to investigate different directions including information fusion, machine learning, and analytical tools design.

The edge analytics is necessary to derive the real-time business values from the IoT generated Big Data. The traditional analytics approaches will not be the right fit because of the high requirements of a large array of analytics applications, network capacity, short time to train the tools, storage at the border of the network, and high computing capabilities. The IoT generated Big Data possess the unique characteristics, such as intermittent noise generation, highly unstructured and dynamic nature, which make the real-time analytics a challenging task. The enterprises must harness the power of locally available distributed computational resources to cope with these challenges to enable the real-time processing and analysis. This has brought opportunities for interdisciplinary research where researchers from different areas of science and technology jointly put their efforts, such as mobile edge computing, data fusion, pattern recognition, machine learning, network softwarization, and communication protocols.

This Special Section in IEEE Access aims to showcase the most recent advances in the interdisciplinary research areas of analytics of the IoT Big Data. This Special Section can bring together researchers from diverse fields and specializations, such as communications engineering, computer science, data sciences, mathematicians, and specialists in areas related to Big Data analytics. We invite researchers from academia, industry, and government to discuss challenging ideas, novel research contributions, demonstration results, and standardization efforts on the real-time analytics of the IoT Big Data and related areas.

The topics of interest include, but are not limited to:

  • Predictive techniques for a real-time Big Data analytics
  • Dimension reduction in the IoT real-time Big Data analytics
  • Information fusion for IoT Big Data analytics
  • Monte Carlo Sampling for real-time Big Data analytics
  • Compressed sensing
  • Low-rank matrix factorization, streaming, and hardness of approximation
  • Efficient learning and clustering
  • Robustness to outliers; convergence and complexity issues
  • Scalable, online, active, decentralized, deep learning and optimization
  • Applications of deep learning and machine learning techniques to perform real-time Big Data in the IoT
  • Architectures and applications for large-scale data analysis
  • Impact of real-time Big Data in the IoT
  • Applications and protocols of Edge computing
  • Distributed data analytical frameworks
  • Semantic tools for analyzing the Big Data
  • In-memory analytics techniques
  • Big Data analytics for cyber defense and cyber intelligence (e.g., Big Data security analytics)
  • Real-time Big Data analytics for anomaly detection in the IoT
  • Big Data sharing, visualization and/or exploration

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Ejaz Ahmed, University of Malaya, Malaysia

Guest Editors:

  1. Ibrar Yaqoob, University of Malaya, Malaysia
  2. Muhammad Imran, King Saud University, Saudi Arabia
  3. Rajkumar Buyya, University of Melbourne, Australia
  4. Michael Devetsikiotis, The University of New Mexico, USA
  5. Luigi Atzori, University of Cagliari, Italy

 

Relevant IEEE Access Special Sections:

  1. Convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things
  2. Big Data Analytics in Internet-of-Things And Cyber-Physical System
  3. Advanced Data Analytics for Large-scale Complex Data Environments

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://mc.manuscriptcentral.com/ieee-access

For inquiries regarding this Special Section, please contact: imejaz@gmail.com