CFP: IEEE TETCI Special Issue on Big Data and Computational Intelligence for Agile Wireless IoT (Oct 15)

I. AIM AND SCOPE

  Wireless networking technology is one of the main components that could empower a wide range of Internet-of- Things (IoT) applications including smart city, smart home, smart grid, e-health, smart transportation, etc. While providing an easily extensible solution for information exchange, wireless networks also have brought some crucial challenges due to the unstable characteristics of wireless communications.

  The first challenge, namely the spatial challenge, comes from the massive number of spatially-spread connected static or mobile devices affected by the limitations and disruptions of the operating environment, including propagation media, disasters, infrastructure failures, and so on. The second challenge, namely the temporal challenge, is due to the time evolution of the temporal features, such as the varying traffic rates, different quality-of-service requirements, and the state changes of the operating environment. Both spatial and temporal challenges can possibly be solved by using Computational intelligence (CI) technologies such as fuzzy logic, artificial neural networks, evolutionary computation, learning theory, probabilistic methods, and so on. On the other hand, big data-based approaches, including deep neural networks and Long Short- Term Memory networks, could facilitate data-driven prediction and performance improvement by capturing time-dependent properties of network elements such as user traffics and behaviors. Meanwhile, new CI technologies should be discussed in order to handle the large volume of IoT big data from various types of devices with different generation speeds and characteristics.

  The design and the operation of a wireless network can benefit from data collected from widely deployed sensors, network devices, social networks, and other sources to address the spatial and temporal challenges. We refer collectively to these data sources as “IoT big data” for convenience. These data can be highly dimensional, heterogeneous, complex, unstructured and unpredictable. The ready availability of IoT big data and the immense dividends on offer motivate a strong interest both in academia and in industry towards solving some of the vexing challenges that stand in the way of leveraging IoT big data to advance the state of the art in wireless network operations and applications.

  CI technologies are expected to provide efficient and powerful tools that scale well with data volume for IoT big data analytics and process, while addressing the challenges brought by the massive amount of data. While CI technologies can achieve a flexible and self-evolving system design, big data can facilitate the use of deep neural networks which is possible to learn the best strategy from complex data. It is envisioned that the combination of IoT big data with a large collection of CI algorithms will reach the level of true agility in wireless IoT.

II. TOPICS

• CI-based solutions for spatial & temporal challenges in wireless IoT, including propagation challenges, MAC & routing problems, mobile edge computing issues, disasters, and infrastructure failures.
• Data-driven prediction and performance improvement for wireless IoT including deep neural networks, Long Short-Term Memory networks, etc.
• Joint neural networks and learning approaches, such as deep reinforcement learning, for addressing challenges in wireless IoT.
• CI technologies for handling a large volume of wireless IoT big data.
• Learning new flexible and self-evolving strategies for resource allocation, network management and planning by analyzing wireless IoT big data with CI.

III. IMPORTANT DATES

• Manuscriptsubmission:October15,2018.
• Notification of authors: January 15, 2019.
• Revised manuscripts due: March 15, 2019.
• Final editorial decision: May15,2019.

IV. SUBMISSION GUIDELINES

  Manuscripts should be prepared according to the “Information for Authors” section of the journal and submissions should be done through the journal manuscript submission system https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “Big Data and Computational Intelligence for Agile Wireless IoT” and clearly marking “Special Issue on Big Data and Computational Intelligence for Agile Wireless IoT” as comments to the Editor-in-Chief.

V. GUEST EDITORS

• Celimuge Wu, The University of Electro-Communications, Japan, celimuge@uec.ac.jp
• Yaochu Jin, University of Surrey, United Kingdom, yaochu.jin@surrey.ac.uk
• Jie Li, University of Tsukuba, lijie@cs.tsukuba.ac.jp
• Kok-Lim Alvin Yau, Sunway University, Malaysia, koklimy@sunway.edu.my
• Junaid Qadir, Information Technology University, Pakistan, junaid.qadir@itu.edu.pk