Emerging Trends in Big Data Driven Edge Intelligence and In-Network Computing

Submission Deadline: 31 May 2023 (closed)

Guest Editors

Dr. Jinghui Zhang, Southeast University, China
Dr. Jun Shen, University of Wollongong, Australia
Dr. Jingya Zhou, Soochow University, China


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.


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

  • Open Access


    An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

    Hai Wang, Xuan Hao, Shuo Ji, Jie Zheng, Yuhui Ma, Jianfeng Yang
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1621-1635, 2023, DOI:10.32604/iasc.2023.037898
    (This article belongs to this Special Issue: Emerging Trends in Big Data Driven Edge Intelligence and In-Network Computing)
    Abstract Trusted Execution Environment (TEE) is an important part of the security architecture of modern mobile devices, but its secure interaction process brings extra computing burden to mobile devices. This paper takes open portable trusted execution environment (OP-TEE) as the research object and deploys it to Raspberry Pi 3B, designs and implements a benchmark for OP-TEE, and analyzes its program characteristics. Furthermore, the application execution time, energy consumption and energy-delay product (EDP) are taken as the optimization objectives, and the central processing unit (CPU) frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through… More >

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