4.8 Article

Cloud-Edge Orchestration for the Internet of Things: Architecture and AI-Powered Data Processing

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 8, Issue 16, Pages 12792-12805

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3014845

Keywords

Cloud computing; Servers; Computer architecture; Internet of Things; Edge computing; Data processing; Medical services; Artificial intelligence (AI); cloud computing; edge computing; Internet of Things (IoT); offloading

Funding

  1. Engineering and Physical Sciences Research Council of United Kingdom [EP/R030863/1]
  2. EPSRC [EP/R030863/1] Funding Source: UKRI

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The Internet of Things is widely utilized in various critical sectors, requiring efficient data processing. AI-powered cloud-edge orchestration provides crucial computing support for IoT applications.
The Internet of Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing, and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralized and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This article first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.

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