Guest Editors
Dr. Jinghui Zhang, Southeast University, China
Dr. Jun Shen, University of Wollongong, Australia
Dr. Jingya Zhou, Soochow University, China
Summary
In recent years, edge computing emerges as a complementary solution to traditional centralized cloud computing, by providing proximate resources close to near services and big data applications. Edge computing supports emerging intelligent big data applications (e.g., self-driving vehicles, autonomous systems, and VR/AR) with stringent requirements of fast response, low delay, high bandwidth, trust-sensitivity, and/or continued operation, despite its intermittent or incomplete connectivity. These trends have converged into the concept of Edge Intelligence, providing intelligence to ubiquitous big data applications with edge computing. The architecture of Edge Intelligence integrates the constituents of in-network computation and network processing in a common framework. Adding computation in the network nodes, beyond packet forwarding, enables more collaborative processing and improves the integration of communication and computation resource management. In-Network Computing meets various big data application requirements in the edge environment. Under such an architecture, the network provides not only a simple connection and can also be considered as an essential constituent of a distributed application.
Big Data Driven Edge Intelligence and In-Network Computing are still an emerging trend and many open issues are yet to be addressed in order to understand how they should evolve. This special issue solicits research papers describing significant and innovative research contributions to this field.
Keywords
Architecture design towards edge intelligence
Compute-First Networking
Deep learning for edge environment
Data-driven edge intelligence
Data plane programming and transportation abstraction for In-Network Computing
Modeling and performance analysis framework for edge computing
System profiling methods for edge intelligence systems
Algorithm design for joint scheduling of communication and computation resources
Federated learning with edge
Training and inference acceleration for intelligent applications
Big data (e.g., graph data, network data, spatio-temporal data, user behavior data etc.) analytics
Artificial intelligence-powered big data engineering and applications
Published Papers
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