AIMS AND SCOPE
Emerging Internet of Things (IoT) applications in various fields, including smart city, smart home, smart grid, e-health, smart transportation, computer vision applications, etc., critically require trustworthy networking solutions that are resilient against disturbances and disruptions, including high mobility, high density, disasters, infrastructure failures, cyberattacks, and other disruptions. The networking framework should be capable of providing more secure, reliable and efficient communications in various network environments, especially for the performance-sensitive and mission-critical applications such as remote surgery and autonomous driving.
Two main challenges exist in enforcing trustworthy IoT. The first challenge comes from the spatial diversity of the entities involved in communications, such as the high mobility of the devices, and the limitations of propagation media and other resources. The second challenge is due to the varying temporal features of the environment. Due to the spatial challenges, the connectivity between network nodes could be unreliable, and therefore the information maintained at each node could be inaccurate, which requires trustworthy solutions that are able to handle the dynamic, imprecise and uncertain information. This can be solved by using computational intelligence (CI) technologies such as fuzzy logic and evolutionary computation. On the other hand, Big Data-based approaches, including deep neural networks, could facilitate data-driven prediction and performance improvement by capturing time-dependent properties of network elements such as user traffic and behaviors. However, the IoT data can be highly dimensional, heterogeneous, complex, unstructured and unpredictable. The integration of two new technologies, namely IoT and Big Data, gives birth to a novel ecosystem, conveniently called IoT Big Data, which calls for novel CI technologies to provide efficient and powerful tools that scale well with massive data volume analytics and processes, while addressing the challenges brought by the massive amount of data. In short, while CI technologies can achieve a flexible and self-evolving system design, Big Data can facilitate the use of deep neural networks through which learning the best strategy from complex data becomes possible.
This special issue will focus on the technical challenges and the synergistic effect of Big Data and CI for trustworthy IoT. It is envisioned that the combination of Big Data with a large collection of CI algorithms will reach the level of true artificial intelligence in IoT. We invite researchers to contribute their original research articles that will facilitate the development of IoT based on CI and big data technologies, including (but not limited to):
- Artificial neural networks for IoT Big Data
- CI for trustworthy IoT
- CI for mobile edge computing
- CI for wireless networking
- CI for security in IoT systems
- CI for sensor and actuator networks
- CI for IoT applications
- Convolutional neural networks for IoT
- Crowdsourced learning for IoT
- Data-driven IoT with CI
- Deep neural networks for trustworthy IoT
- Deep reinforcement learning for IoT
- Development of CI for IoT environments
- Domain adaptation for IoT Big Data
- Evolutionary computing for IoT Big Data
- Evolutionary models for IoT Big Data
- Fuzzy logic for IoT Big Data
- Learning theory for IoT Big Data
- Machine learning for IoT Big Data
- Probabilistic methods for IoT Big Data
- Recurrent neural networks for IoT
- Sequence-to-sequence learning for IoT Big Data
Submission Deadline: December 31st, 2018
Notification of the First Review Results: March 15th, 2019
Submission of Revised Manuscripts: April 15th, 2019
Notification of Second Review Results: May 15th, 2019
Submission of Final Manuscript: June 15th, 2019
Special Issue Publication: November 2019 Issue
Dr. Celimuge Wu, The University of Electro-Communications, Japan, firstname.lastname@example.org
Dr. Guoliang Xue, Arizona State University, USA, email@example.com
Dr. Jie Li, University of Tsukuba, Japan, firstname.lastname@example.org
Dr. Kok-Lim Alvin Yau, Sunway University, Malaysia, email@example.com
Dr. Junaid Qadir, Information Technology University, Pakistan, firstname.lastname@example.org
1. The IEEE Computational Intelligence Magazine requires all prospective authors to submit their manuscripts in electronic format, as a PDF file. The created PDF file must be a single file for the complete submitted paper, including figures and bibliography. Before the manuscript is submitted, prospective authors should make sure that the PDF file is (1) printable, and (2) its first page contains the title, authors' names and the corresponding author's email address, abstract, and up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website. Submission should be made via https://easychair.org/conferences/?conf=ieeecimsiiot2019.
2. Send also an email to guest editor C. Wu (email@example.com) with subject “IEEE CIM special issue submission” to notify about your submission.
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