The next generation of mobile networks, i.e., sixth generation (6G), is expected by 2030, with already burgeoning research efforts toward this goal. Along with various other candidate technologies, blockchain is envisioned to enable and enhance numerous key functionalities of 6G. Thus, the main objective of this article is threefold: to categorize the different aspects of 6G into four emerging directions that anticipate significant advancements leveraging blockchain, to discuss the potential role of blockchainized 6G under each key emerging direction, and to expound on the technical challenges in blockchaining 6G along with possible solutions.

Building on

The IEEE Consumer Electronics Magazine (IEEE CEM) of the IEEE Consumer Technology Society (CTSoc) bimonthly publishes peerreviewed articles that present emerging trends, key insights, tutorials, practical experiences, design, and industry-related research & developments of mainstream consumer electronic products, technologies, and related fields of interest to the membership of the IEEE CTSoc and broad engineering audience. CEM aims to educate and entertain on general topics related to consumer technologies and electronic products. of articles organized around a timely narrow-scope technical topic of interest to the magazine readership. A SI may be composed of 6 to 12 articles. SIs can give articles special prominence and should provide a better opportunity for readers to explore special and emerging topics including, but not limited to, the followings:

Emerging edge computing (EEC) has been introduced as an innovative paradigm for the healthcare applications of the Internet of Things (IoT) that aims to distribute the network resources at the network edges to improve security, communication, and decision-making processes. The operation of healthcare IoT applications typically needs the presence of interoperable modules. Despite numerous benefits, these applications face many security challenges at the network edge. In this context, advanced artificial intelligence (AI) techniques can be used at the network edges for these applications to efficiently utilize the available resources securely. To this end, we aim to present a detailed survey of healthcare IoT applications in the context of AI-enabled EEC technology to identify unresolved security challenges that need attention from the research community and healthcare stakeholders, and then suggest potential research directions to give a clear future insight.

Deep learning models require vast amounts of annotated training data. Gathering and annotating the data from the real world is an expensive and time-consuming process. Thus, synthetically generated data is being researched more and more. This paper tries to answer the question of whether and to what extent synthetically generated data can help in developing the advanced driver-assistance system (ADAS) algorithms for autonomous vehicles, for the object detection task based on deep learning. Two state-of-the-art deep learning object detectors were trained on various combinations of real-world and synthetic data. A total of 12 detectors were tested on real-world test images. Results show that synthetic data can contribute to better detector performance until a certain ratio between real-world and synthetic data is reached.