Green industrial networking: recent advances, taxonomy, and open research challenges

Ahmed, Ejaz; Yaqoob, Ibrar; Ahmed, Arif; Gani, Abdullah; Imran, Muhammad; Guizani, Sghaier

doi:10.1109/mcom.2016.7588227

Cited by 15 publications

(6 citation statements)

References 13 publications

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“…On the production side, ML can potentially improve the efficiency of HVAC systems and other industrial control mechanisms-given necessary data about all relevant processes. Deep neural networks could be used for adaptive control in a variety of industrial networking applications [360], enabling energy savings through self-learning about devices' surroundings. To reduce GHG emissions from HVAC systems, researchers suggest combining optimization-based control algorithms with ML techniques such as image recognition, regression trees, and time delay neural networks [361,362] (see also 3.1).…”

Section: High Leveragementioning

confidence: 99%

Tackling Climate Change with Machine Learning

Rolnick¹,

Donti²,

Kaack³

et al. 2019

Preprint

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Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

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Section: High Leveragementioning

confidence: 99%

Tackling Climate Change with Machine Learning

Rolnick¹,

Donti²,

Kaack³

et al. 2019

Preprint

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“…42:8 D. Rolnick et al see [27] for an overview. 8 ML can both reduce emissions from today's standby generators and enable the transition to carbon-free systems by helping improve necessary technologies (namely forecasting, scheduling, and control) and by helping create advanced electricity markets that accommodate both variable electricity and flexible demand.…”

Section: Variable Sourcesmentioning

confidence: 99%

“…DeepMind has used RL to optimize cooling centers for Google's internal servers by predicting and optimizing the power usage effectiveness, thus lowering HFC emissions and reducing cooling costs [227,268]. 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.…”

Section: High Leveragementioning

confidence: 99%

Tackling Climate Change with Machine Learning

et al. 2022

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Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

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“…To cover the energy management aspect of IoT-assisted smart cities, Ejaz et al [13] presented an efficient optimization and scheduling framework. The green industrial networking aspect was investigated in [14]. Last but not the least, a perspective of 5G technologies with soft and green themes was explored in [15].…”

Section: Current Major Challenges and Requirementsmentioning

confidence: 99%

Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions

2019

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The excessive use of digital devices such as cameras and smartphones in smart cities has produced huge data repositories that require automatic tools for efficient browsing, searching, and management. Data prioritization (DP) is a technique that produces a condensed form of the original data by analyzing its contents. Current DP studies are either concerned with data collected through stable capturing devices or focused on prioritization of data of a certain type such as surveillance, sports, or industry. This necessitates the need for DP tools that intelligently and cost-effectively prioritizes large variety of data for detecting abnormal events, and hence effectively manages them, thereby making the current smart cities greener. In this paper, we first carried out an in-depth investigation on the recent approaches and trends of DP for data of different nature, genre, and domain of two decades in green smart cities. Next, we proposed an energy-efficient DP framework by intelligent integration of Internet of things, artificial intelligence, and big data analytics. Experimental evaluation on real-world surveillance data verified the energy-efficiency and applicability of this framework in green smart cities. Finally, this article highlights the key challenges of DP, its future requirements, and propositions for integration into green smart cities.

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Green industrial networking: recent advances, taxonomy, and open research challenges

Cited by 15 publications

References 13 publications

Tackling Climate Change with Machine Learning

Tackling Climate Change with Machine Learning

Tackling Climate Change with Machine Learning

Intelligent and Energy-Efficient Data Prioritization in Green Smart Cities: Current Challenges and Future Directions

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