Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay
Open accessAbstract
The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency issues which affect the overall QoS for various applications in IIoT. To cope with these challenges, a recent paradigm shift in computing, popularly known as edge computing, has emerged. Edge computing can be viewed as a complement to cloud computing rather than as a competition. The cooperation and interplay among cloud and edge devices can help to reduce energy consumption in addition to maintaining the QoS for various applications in the IIoT environment. However, a large number of migrations among edge devices and cloud servers leads to congestion in the underlying networks. Hence, to handle this problem, SDN, a recent programmable and scalable network paradigm, has emerged as a viable solution. Keeping focus on all the aforementioned issues, in this article, an SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support. In the proposed solution, a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented. The proposed scheme is evaluated with respect to two optimization objectives, that is, the trade-off between energy efficiency and latency, and the trade-off between energy efficiency and bandwidth. The results obtained prove the effectiveness of the proposed flow scheduling scheme in the IIoT environment.
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The reliance on devices with limited computational capabilities, memory storage, and battery power in IIoT systems introduces challenges to reliability, resulting in energy consumption and time delay issues during computation offloading.
“Moreover, the reliance on devices with limited computational capabilities, memory storage, and battery power in IIoT systems introduces additional challenges to reliability.These constraints can result in energy consumption and time delay issues during computation offloading, further exacerbating reliability concerns [34].”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [34]
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- Authors
- Kaur, K., Garg, S., Aujla, G. S., Kumar, N., Rodrigues, J. J. P. C., & Guizani, M.
- Affiliations
- Thapar Institute of Engineering & Technology (IN)
- ITMO University (RU)
- University of Idaho (US)
- ORCID
- Venue
- IEEE Communications Magazine
- Published
- 2018-02
- Volume
- 56
- Issue
- 2
- Pages
- 44-51
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2787444476
- Cited by
- 353
- References
- 13
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 28 · 2023: 41 · 2022: 47 · 2021: 81 · 2020: 61 · 2019: 57 · 2018: 17
- FWCI
- 33.45
- Type
- journal article
- Language
- en
- Open access
- Locations
- IEEE Communications Magazine (publishedVersion)
- Espace ÉTS (ETS) (submittedVersion)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- Funders
- Cisco Systems
- Council of Scientific and Industrial Research, India
- Fundação para a Ciência e a Tecnologia
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- edge computing, Industrial Internet of Things, software-defined networking, big data streaming, flow scheduling, routing, multi-objective evolutionary algorithms, energy efficiency, latency, bandwidth, network congestion, quality of service, middleware
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