Machine Learning in Robotics with Fog/Cloud Computing and IoT Article Swipe
Kiran Deep Singh
,
Prabh Deep Singh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.4108/airo.3621
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.4108/airo.3621
Robotics has been transformed by machine learning (ML), enabling intelligent and adaptive autonomous systems. By delivering massive computational resources and real-time data, fog/cloud computing and the Internet of Things boost ML-based robotics. Intelligent and linked robotics have emerged from fog/cloud computing, IoT, and machine learning. Robots using distributed computing, real-time IoT data, and advanced machine learning algorithms could alter industries and improve automation. To maximize its potential, this revolutionary combination must overcome several obstacles. This paper discusses the benefits and drawbacks of integrating technologies. It offer rapid model training and deployment for robots ML algorithms like deep learning and reinforcement learning. Case studies demonstrate how this combination might enhance robotics across industries. This study discusses the benefits and drawbacks of fog/cloud computing, IoT, and machine learning in robots. We propose solutions for security and privacy, resource management, latency and bandwidth, interoperability, energy efficiency, data quality, and bias. By proactively addressing these difficulties, we can establish a secure, efficient, and privacy-conscious robotic ecosystem where robots seamlessly interact with the physical world, improving productivity, safety, and human-robot collaboration. As these technologies progress, appropriate integration and ethical principles are needed to maximize their benefits to society.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4108/airo.3621
- https://publications.eai.eu/index.php/airo/article/download/3621/2731
- OA Status
- diamond
- Cited By
- 2
- References
- 32
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4389307495Canonical identifier for this work in OpenAlex
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https://doi.org/10.4108/airo.3621Digital Object Identifier
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Machine Learning in Robotics with Fog/Cloud Computing and IoTWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-12-04Full publication date if available
- Authors
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Kiran Deep Singh, Prabh Deep SinghList of authors in order
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https://doi.org/10.4108/airo.3621Publisher landing page
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https://publications.eai.eu/index.php/airo/article/download/3621/2731Direct link to full text PDF
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diamondOpen access status per OpenAlex
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https://publications.eai.eu/index.php/airo/article/download/3621/2731Direct OA link when available
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Cloud computing, Artificial intelligence, Computer science, Robotics, Interoperability, Robot, Machine learning, Automation, Software deployment, Distributed computing, Software engineering, Engineering, World Wide Web, Operating system, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learning | 6, 55, 97, 125 |
| abstract_inverted_index.maximize | 64, 188 |
| abstract_inverted_index.overcome | 71 |
| abstract_inverted_index.physical | 168 |
| abstract_inverted_index.privacy, | 134 |
| abstract_inverted_index.quality, | 144 |
| abstract_inverted_index.resource | 135 |
| abstract_inverted_index.robotics | 35, 109 |
| abstract_inverted_index.security | 132 |
| abstract_inverted_index.society. | 192 |
| abstract_inverted_index.systems. | 13 |
| abstract_inverted_index.training | 88 |
| abstract_inverted_index.computing | 23 |
| abstract_inverted_index.discusses | 76, 114 |
| abstract_inverted_index.drawbacks | 80, 118 |
| abstract_inverted_index.ecosystem | 161 |
| abstract_inverted_index.establish | 154 |
| abstract_inverted_index.fog/cloud | 22, 39, 120 |
| abstract_inverted_index.improving | 170 |
| abstract_inverted_index.learning. | 44, 100 |
| abstract_inverted_index.progress, | 179 |
| abstract_inverted_index.real-time | 20, 49 |
| abstract_inverted_index.resources | 18 |
| abstract_inverted_index.robotics. | 31 |
| abstract_inverted_index.solutions | 130 |
| abstract_inverted_index.addressing | 149 |
| abstract_inverted_index.algorithms | 56, 94 |
| abstract_inverted_index.autonomous | 12 |
| abstract_inverted_index.bandwidth, | 139 |
| abstract_inverted_index.computing, | 40, 48, 121 |
| abstract_inverted_index.delivering | 15 |
| abstract_inverted_index.deployment | 90 |
| abstract_inverted_index.efficient, | 157 |
| abstract_inverted_index.industries | 59 |
| abstract_inverted_index.obstacles. | 73 |
| abstract_inverted_index.potential, | 66 |
| abstract_inverted_index.principles | 184 |
| abstract_inverted_index.seamlessly | 164 |
| abstract_inverted_index.Intelligent | 32 |
| abstract_inverted_index.appropriate | 180 |
| abstract_inverted_index.automation. | 62 |
| abstract_inverted_index.combination | 69, 106 |
| abstract_inverted_index.demonstrate | 103 |
| abstract_inverted_index.distributed | 47 |
| abstract_inverted_index.efficiency, | 142 |
| abstract_inverted_index.human-robot | 174 |
| abstract_inverted_index.industries. | 111 |
| abstract_inverted_index.integrating | 82 |
| abstract_inverted_index.integration | 181 |
| abstract_inverted_index.intelligent | 9 |
| abstract_inverted_index.management, | 136 |
| abstract_inverted_index.proactively | 148 |
| abstract_inverted_index.transformed | 3 |
| abstract_inverted_index.technologies | 178 |
| abstract_inverted_index.computational | 17 |
| abstract_inverted_index.difficulties, | 151 |
| abstract_inverted_index.productivity, | 171 |
| abstract_inverted_index.reinforcement | 99 |
| abstract_inverted_index.revolutionary | 68 |
| abstract_inverted_index.technologies. | 83 |
| abstract_inverted_index.collaboration. | 175 |
| abstract_inverted_index.interoperability, | 140 |
| abstract_inverted_index.privacy-conscious | 159 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.66391763 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |