Massive Learning and Computing for the Reliable Internet of Everything

Dear Colleagues,

The Internet of Everything (IoE) technology is continuously expanding its impact on our daily lives, aided by the increase in the number of connected devices with higher intelligence compared to the past. Additionally, as the performance of devices has advanced significantly, their application also continues to expand and vary widely (e.g., collaborative data collection, smart city, smart factory, cooperating robots, or drone swarm performance). To perform their given missions more successfully, devices must improve their collective intelligence by collaborating with each other. In order to do this, they need to share collected data, learning methods, environmental information, mission objectives, and mission progresses. In addition, to increase the overall performance and broaden the application scope, high-efficiency, high-performance, and stable networks in massive IoE systems are necessary. Moreover, in order to increase the reliability of the results of learning and computing, the process of delivering data over the network must be evaluated, and its trustworthiness guaranteed.

The focus of this Special issue will be on dealing with the requirements, challenges, constraints, theoretical issues, innovative applications, and experimental results associated with the massive learning and computing for the Internet of Everything.

Topics of interest include but are not limited to:

• Massive learning and computing methods, algorithms, and systems for the IoE;
• Collaborative/federated/distributed learning in the IoE;
• Dependable design and implementation for the IoE in the perspective of reliability, availability, and survivability;
• Security and privacy schemes on massive learning and computing in the IoE;
• Trusted devices, networks, and computing resource sharing and management for the IoE, such as blockchain-based management for devices, networks, and computing;
• Low latency and highly reliable communications for collaborative computing in the IoE;
• Collective intelligence IoE in 5G and beyond cellular communication systems;
• Trusted and collaborative framework for deep learning in the IoE;
• Various applications supported by collaborative learning and computing in IoE systems;
• Data-driven analysis and model on massive learning and computing in the IoE.

Prof. Dr. Hwangnam Kim
Dr. Woonghee Lee
Dr. Seungho Yoo
Dr. Eun-Chan Park
Guest Editors

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