- Submission deadline: 30 June 2019
- Acceptance notification: 15 Aug 2019
- Camera-ready version: 15 Sept 2019
Aim and scope
Emerging Internet-of-Things (IoT) applications in various fields, including smart city, smart home, smart grid, e-health, smart transportation, etc., critically require trustworthy networking solutions that are resilient against high mobility, high density, disasters, infrastructure failures, cyberattacks, and other disruptions. The networking framework should be capable of providing more secure in addition to more reliable and efficient communications in various network environments. Especially for the performance-sensitive and mission-critical applications, such as remote surgery and autonomous driving, more trustworthy and intelligent networking solutions are in an urgent need.
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 nodes, the large number of devices, and the limitations of propagation media and other resources. The second challenge lies in the varying temporal features of the environment. Due to the spatial challenges, the connectivity between network nodes tends to be unreliable, for which the information maintained at each node can be inaccurate, which requires trustworthy solutions that are able to handle the dynamics, imprecision and uncertainties. This can be potentially solved by employing 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 traffics and behaviours. However, the IoT data can be highly dimensional, heterogeneous, complex, unstructured and unpredictable. The obstacle in analysing “IoT big data” calls for CI technologies which are expected to provide efficient and powerful tools that scale well with data volume for IoT big data analytics and processing.
This workshop will focus on the technical breakthroughs and the synergistic effects of big data and CI for IoT. While CI technologies can achieve a flexible and self-evolving system design, big data can facilitate the implementation of deep neural networks, which makes it possible to learn the optimal strategies from complex data. It is envisioned that the combination of big data with a large collection of CI algorithms will reach the human-level intelligence in IoT.
Topics of Interest
We invite researchers to contribute their novel research results that advance the development of trustworthy IoT based on CI and big data technologies, including (but not limited to):
- Artificial neural networks for IoT big data
- Big data and CI for trustworthy IoT
- Big data and CI for mobile edge computing
- Big data and CI for wireless networking
- Big data and CI for security in IoT systems
- Big data and CI for sensor and actuator networks
- Data-driven Trustworthy IoT with CI
- Deep neural networks for trustworthy IoT
- 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
All accepted papers will be published in the proceedings of the GLOBECOM workshop and made available through IEEE Xplore.
- Jie Wu Temple University, USA Email: firstname.lastname@example.org
- Xianfu Chen VTT Technical Research Centre of Finland, Finland Email: email@example.com
Workshop TPC Chairs
- Celimuge Wu The University of Electro-Communications, Japan Email: firstname.lastname@example.org
- Chase Q. Wu New Jersey Institute of Technology, USA Email: email@example.com
The workshop follows the formatting guidelines of IEEE GLOBECOM 2019. Submissions should be original and limited to 6 double-column pages in IEEE paper templates. All submissions should be written in English using 10-point font. Authors are invited to submit their manuscripts in PDF format through EDAS conference system (https://edas.info/N26308).