Real-time Optimisation for Industrial Internet of Things (IIoT): Overview, Challenges and Opportunities

Nguyen, Long and Kortun, Ayse (2021) Real-time Optimisation for Industrial Internet of Things (IIoT): Overview, Challenges and Opportunities. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 7 (25). p. 167654. ISSN 2410-0218

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Abstract

Industrial Internet-of-Things (IIoT) with massive data transfers and huge numbers of connected devices, incombination with the high demand for greater quality-of-services, signal processing is no longer producing small data sets but rather, very large ones (measured in gigabytes or terabytes), or even higher. This has posed critical challenges in the context of optimisation. Communication scenarios such as online applications come with the need for real-time optimisation. In such scenarios, often under a dynamic environment, a strict real-time deadline is the most important requirement to be met. To this end, embedded convex optimisation, which can be redesigned and updated within a fast time-scale given sufficient computing power, is a candidate to deal with the challenges in real-time optimisation applications. Real-time optimisation is now becoming a reality in signal processing and wireless networks of IIoT. Research into new technologies to meet future demands is receiving urgent attention on a global scale, especially when 5G networks are expected to be in place in 2020. This work addresses the fundamentals, technologies and practically relevant questions related to the many challenges arising from real-time optimisation communications for industrial IoT.

Item Type: Article
Uncontrolled Keywords: Industrial Internet-of-Thing, convex optimization, realtime optimization, cloud computing, 5G network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 21 Jan 2021 06:55
Last Modified: 21 Jan 2021 06:55
URI: https://eprints.eudl.eu/id/eprint/824

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