Visual E<sup>2</sup>C: AI-Driven Visual End-Edge-Cloud Architecture for 6G in Low-Carbon Smart Cities

Yang, Zheming; Hu, Dongfeng; Guo, Qing; Zuo, Lulu; Ji, Wen

doi:10.1109/mwc.019.2200518

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…[58], [61], [62], [65], [67], [27], [74], [90], [96], [106] Application Architecture 11 [62], [65], [25], [74], [36], [37], [80], [90], [96], [103], [6] Technology Architecture 54 [108], [60], [55], [23], [62], [20], [24], [63], [65], [112], [109], [67], [69], [70], [71], [29], [111], [73], [32], [75], [77], [34], [35], [81], [4], [5], [82], [39], [40], [83], [41], <...…”

Section: ) Results Reporting On Rq2mentioning

confidence: 99%

“…Stationary energy 42 [58], [60], [55], [62], [69], [70], [27], [110], [29], [111], [73], [32], [77], [79], [81], [4], [39], [83], [41], [56], [88], [3], [89], [91], [45], [46], [94], [95], [97], [49], [57], [52], [100], [53], [54], [102], [103], [92], [104], [105], [106], [6] Transportation 10…”

Section: Number Of Research Referencesmentioning

confidence: 99%

“…One prominent trend involves the integration of AI algorithms in predictive modeling to optimize energy consumption patterns and identify opportunities for emission reduction across various sectors, including buildings, transportation, and industrial processes [60], [43], [74]. Additionally, AI-driven optimization techniques are being employed in smart grids and energy management systems, allowing real-time analysis and optimal distribution of energy resources to minimize environmental impact [111]. Furthermore, the trend of employing Multi-Agent Architecture, driven by AI, has emerged as a novel approach to optimize energy usage and minimize waste across supply chains, contributing significantly to environmental sustainability [68], [78], [43].…”

Section: ) Ai-driven Transformationsmentioning

confidence: 99%

See 2 more Smart Citations

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Somantri,

Surendro

2024

IEEE Access

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Computer Science Architecture (CSA), encompassing data, application, technology, and business architecture, is a vital tool for addressing climate change challenges. It aims to reduce greenhouse gas emissions from sectors such as stationary energy, transportation, industry, product use, waste, and land use. CSA principles extend to advanced technologies like artificial intelligence (AI), the Internet of Things (IoT), Machine Learning (ML), data centers, blockchain, multi-agent systems, sensors, and smart grids. This review explores CSA's role in mitigating greenhouse gas emissions for a sustainable environment. It analyzes implications, indicators, and methodologies for data, application, technology, and business architecture, highlighting their direct impact on creating an environmentally friendly technological landscape. The study also delves into emerging trends and suggestions for future research, contributing to the discourse on leveraging technology for a greener future.INDEX TERMS Greenhouse gas emissions, computer science architecture, sustainable environment, review.

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Section: ) Results Reporting On Rq2mentioning

confidence: 99%

Section: Number Of Research Referencesmentioning

confidence: 99%

Section: ) Ai-driven Transformationsmentioning

confidence: 99%

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Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Somantri,

Surendro

2024

IEEE Access

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“…This model incorporated a novel trimodal module and a twostep hybrid model, together with a lightweight student network assisted by neighboring collaborative distillation. In [25], the E2C model, together with systematic evaluation and intellectual computing models is proposed, for carbon emissions in visual computing.…”

Section: Related Workmentioning

confidence: 99%

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Alsubai,

Dutta,

Alghayadh

et al. 2024

IEEE Access

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Smart Cities refer to urban areas which exploits recent technologies for improving the performance, sustainability, and livability of their infrastructure and services. Crowd Density Analysis (CDA), a vital component of Smart Cities, involves the use of sensors, cameras, and data analytics to monitor and analyze the density and movement of people in public spaces. CDA utilizing DL harnesses the control of neural networks to mechanically and exactly evaluate the density of crowds in numerous settings, mainly in smart cities. DL techniques like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), are trained on vast datasets of crowd videos or images to learn complex designs and features. These models can forecast crowd density levels, recognize crowd anomalies, and offer real-time visions into crowd behavior. This study designs an Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities (AICDA-SSC) technique. The aim of the AICDA-SSC method is to analyze the crowd density and classify it into multiple classes by the use of hyperparameter-tuned DL models. To accomplish this, the AICDA-SSC technique applies contrast enhancement using the CLAHE approach. Besides, the complex and intrinsic features can be derived by the use of the Inception v3 model and its hyperparameters can be chosen by the use of the marine predator's algorithm (MPA). For crowd density detection and classification, the AICDA-SSC technique applies a gated recurrent unit (GRU) model. Finally, a chaotic sooty tern optimizer algorithm (CSTOA) based hyperparameter selection procedure takes place to increase the effectiveness of the GRU system. The experimental evaluation of the AICDA-SSC technique takes place on a crowd-density image dataset. The experimentation values showcase the superior performance of the AICDA-SSC method to the recently developed DL models.

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Is smart city low-carbon? Evidence from China

Zhu,

Li,

Zhu

et al. 2024

Heliyon

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Visual E²C: AI-Driven Visual End-Edge-Cloud Architecture for 6G in Low-Carbon Smart Cities

Cited by 7 publications

References 12 publications

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Is smart city low-carbon? Evidence from China

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Visual E2C: AI-Driven Visual End-Edge-Cloud Architecture for 6G in Low-Carbon Smart Cities

Cited by 7 publications

References 12 publications

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Greenhouse Gas Emission Reduction Architecture in Computer Science: A Systematic Review

Design of Artificial Intelligence Driven Crowd Density Analysis for Sustainable Smart Cities

Is smart city low-carbon? Evidence from China

Contact Info

Product

Resources

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Visual E²C: AI-Driven Visual End-Edge-Cloud Architecture for 6G in Low-Carbon Smart Cities