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Edge-based IoT Systems with Cross-Designs of Communication, Computing, and Control

Submission Deadline: 31 December 2024 View: 510 Submit to Special Issue

Guest Editors

Prof. Jia Wu, Monash university, Australia
Prof. Peiyuan Guan, University of Oslo, Norway
Prof. Fangfang Gou, Guizhou University, China

Summary

Cloud-Fog Automation is a recently suggested digital architecture for IoT automation, facilitated by cutting-edge communication technologies. This approach deviates significantly from the traditional models, aiming to expedite the integration and synergistic effects of communication, computation, and control towards the forthcoming generation of industrial cyber-physical systems. This architecture is network-centric, harnessing the power of both cloud and fog technologies cooperatively to attain consistent and dependable connectivity, network computation, and network control. As a result, applications no longer have to rely on specific vendor hardware and software and could even be offered as a service with flexible technical and infrastructural demands, thereby providing immense business advantages. Given the introduction of new wireless technologies facilitating near-deterministic ultra-dependable low-latency communications, there is a rising demand for the joint design of optimal control and resource management strategies for time-sensitive IoT systems, emphasizing that the functional safety and security of these systems should remain uncompromised. Another priority is to enhance system-wide or application-level performance, fueled by 'goal-oriented communication' in the underlying communication technologies. Nonetheless, there are numerous unresolved research challenges to realize Cloud-Fog Automation, necessitating a thoughtful reconsideration and research to co-design and harmonize communication, computation and control. In specific, investigations on deterministic networking, computation virtualization, and network control/estimation for time-sensitive IoT systems could pave the way for innovative applications such as digital twins, robotics, virtual and augmented reality in advanced manufacturing, and etc.

 

This Special Issue aims to provide a forum for the latest advances in industrial communications, computing and control co-design, and endeavors to promote research, innovations, and applications to bridge the gap between theory and applications. We solicit high-quality original research papers on topics including, but not limited to:

Novel design metrics and optimization methodologies for communication, computing, and control co-design in next-generation IoT systems with Cloud-Fog Automation

Cross-layer optimization techniques of new IoT wireless technologies with control systems and computing frameworks

Cross-design of network architectures with control and computing requirements embedded for interoperability, heterogeneity, and scalability

Cross-design of virtualization technologies and control applications using optimal network scheduling and intelligent control algorithms

Cross-design of communication, computing, and control paradigms for efficient resource scheduling, sharing, service provisioning and management

Tight integration of security and privacy-preserving solutions across communications, computing, and control domains for system integrity


Keywords

IoT, Edge Computing, Cloud Computing, Real-time tasks, AI

Published Papers


  • Open Access

    ARTICLE

    Dynamic Task Offloading Scheme for Edge Computing via Meta-Reinforcement Learning

    Jiajia Liu, Peng Xie, Wei Li, Bo Tang, Jianhua Liu
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058810
    (This article belongs to the Special Issue: Edge-based IoT Systems with Cross-Designs of Communication, Computing, and Control)
    Abstract As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective… More >

  • Open Access

    ARTICLE

    An Asynchronous Data Transmission Policy for Task Offloading in Edge-Computing Enabled Ultra-Dense IoT

    Dayong Wang, Kamalrulnizam Bin Abu Bakar, Babangida Isyaku, Liping Lei
    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4465-4483, 2024, DOI:10.32604/cmc.2024.059616
    (This article belongs to the Special Issue: Edge-based IoT Systems with Cross-Designs of Communication, Computing, and Control)
    Abstract In recent years, task offloading and its scheduling optimization have emerged as widely discussed and significant topics. The multi-objective optimization problems inherent in this domain, particularly those related to resource allocation, have been extensively investigated. However, existing studies predominantly focus on matching suitable computational resources for task offloading requests, often overlooking the optimization of the task data transmission process. This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network, resulting in increased service times due to elevated network transmission latencies and idle computational resources.… More >

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