Green industrial networking: recent advances, taxonomy, and open research challenges
Abstract
The consciousness of environmental problems has attracted the industry's attention toward the reduction of unnecessary energy emission by enabling green industrial networking. The reduction of unnecessary energy emitted by industrial networks can be a possible solution to many environmental issues. Green industrial networking is in its infancy, and an overview of the domain is still lacking. In this article, we discuss recent advances in industrial and green networking paradigms to investigate the impact on global communities. We also classify the literature by devising a taxonomy based on networking technologies, machines, network types, topologies, field bus types, transmission media, and hierarchical levels. Moreover, we identify and discuss key enablers (adaptive links, resource-based energy conservation, energy-efficient scheduling, energy-aware systems, energy-aware proxying, energy-conservative approaches, and low-power wireless protocols) for green industrial networking. Furthermore, we discuss challenges that remain to be addressed as future research directions.
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Deep neural networks could be used for adaptive control in a variety of industrial networking applications, enabling energy savings through self-learning about devices' surroundings.
“Deep neural networks could also be used for adaptive control in a variety of industrial networking applications [8], enabling energy savings through self-learning about devices' surroundings.”
From Tackling climate change with machine learning (2023) · cites this paper as [8]
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- Authors
- Ahmed, E., Yaqoob, I., Ahmed, A., Gani, A., Imran, M., & Guizani, S.
- Affiliations
- University of Malaya (MY)
- National Institute Of Technology Silchar (IN)
- King Saud University (SA)
- Alfaisal University (SA)
- Venue
- IEEE Communications Magazine
- Published
- 2016-10
- Volume
- 54
- Issue
- 10
- Pages
- 38-45
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2531323243
- Cited by
- 15
- References
- 16
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 3 · 2020: 1 · 2019: 3 · 2018: 4 · 2017: 1
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Magazine (publishedVersion)
- FedUni ResearchOnline (Federation University Australia) (submittedVersion)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy Efficiency and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Keywords
- energy efficiency, industrial networks, taxonomy (general classification scheme), fieldbus, energy-aware scheduling, energy conservation, low-power wireless protocols, network topology
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