An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication System Article Swipe
Sudipto Bhattacharyya
,
Senthil Athithan
,
Souvik Pal
,
Bikramjit Sarkar
,
D. Akila
,
S. Chowdhury
,
Karthik Chandran
,
Gurusamy Saravanakumar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1155/2023/7556728
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1155/2023/7556728
The smart manufacturing system can become a linked network with the help of the Internet of Things (IoT). Devices connected to the IoT are susceptible to various attacks and assaults. An effective protection plan is needed to ensure that the billions of IoT nodes are protected from these hazards. The security mechanisms on IoT devices are ineffective due to resource limitations. As a result, the academic community has recently paid attention to the cloud-, fog-, and edge-based IoT systems. A robust cloud provider is in the cloud or fog to perform computationally demanding activities, including safety, data analysis, decision-making process, and monitoring. Hash identities and upgraded Rivest–Shamir–Adleman (RSA) have been used to secure the IoT device’s data. A four-prime integer of 512 bits makes up the proposed security algorithm. A hash signature is used to provide device authentication. An effective clustering method for sensing devices based on the node level, separation from the clusters, remaining energy, and fitness has been presented for long network life. The suggested swarm-based method determines the sensor nodes’ fitness. A deep neural network- (DNN-) based resource scheduling algorithm (DNN-RSM) is meant to reduce the delay and communications overhead for IoT components in the hybrid cloud system. For optimum resource allocation, all queries originating from the cluster head are categorised using DNN based on their storage, processing, and bandwidth needs. The suggested structure delivers better outcomes, particularly regarding energy use, delay, and safety level. The results of the simulation provide credence to the concept that the proposed strategy is superior to the current system. The suggested scheme includes stringent security, decreased energy usage, decreased latency, and efficient resource utilization.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2023/7556728
- https://downloads.hindawi.com/journals/scn/2023/7556728.pdf
- OA Status
- hybrid
- Cited By
- 6
- References
- 30
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379057736Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2023/7556728Digital Object Identifier
- Title
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An IoT-Enabled Intelligent and Secure Manufacturing Model Using Blockchain in Hybrid Cloud Communication SystemWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-01Full publication date if available
- Authors
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Sudipto Bhattacharyya, Senthil Athithan, Souvik Pal, Bikramjit Sarkar, D. Akila, S. Chowdhury, Karthik Chandran, Gurusamy SaravanakumarList of authors in order
- Landing page
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https://doi.org/10.1155/2023/7556728Publisher landing page
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https://downloads.hindawi.com/journals/scn/2023/7556728.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/scn/2023/7556728.pdfDirect OA link when available
- Concepts
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Computer science, Cloud computing, Distributed computing, Computer network, Overhead (engineering), Hash function, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 1, 2024: 4, 2023: 1Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2945357379, https://openalex.org/W2916963250, https://openalex.org/W3036425307, https://openalex.org/W2788276261, https://openalex.org/W2953287140, https://openalex.org/W3113491849, https://openalex.org/W2946471309, https://openalex.org/W3004197293, https://openalex.org/W2940867210, https://openalex.org/W2944083578, https://openalex.org/W3033176519, https://openalex.org/W2890982116, https://openalex.org/W2999044580, https://openalex.org/W2976509638, https://openalex.org/W2990913508, https://openalex.org/W2962969396, https://openalex.org/W3189400418, https://openalex.org/W2894453619, https://openalex.org/W2613484160, https://openalex.org/W2980182496, https://openalex.org/W3085854806, https://openalex.org/W3126575951, https://openalex.org/W3129986604, https://openalex.org/W4290471671, https://openalex.org/W2980126319, https://openalex.org/W4224322353, https://openalex.org/W4284895726, https://openalex.org/W4220986870, https://openalex.org/W4205858455, https://openalex.org/W4206364378 |
| referenced_works_count | 30 |
| abstract_inverted_index.A | 79, 117, 129, 174 |
| abstract_inverted_index.a | 6, 62 |
| abstract_inverted_index.An | 30, 138 |
| abstract_inverted_index.As | 61 |
| abstract_inverted_index.in | 84, 196 |
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| abstract_inverted_index.512 | 121 |
| abstract_inverted_index.DNN | 215 |
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| abstract_inverted_index.IoT | 22, 42, 53, 77, 114, 194 |
| abstract_inverted_index.The | 0, 49, 165, 224, 238, 258 |
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| abstract_inverted_index.that | 38, 248 |
| abstract_inverted_index.use, | 233 |
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| abstract_inverted_index.(RSA) | 107 |
| abstract_inverted_index.based | 145, 179, 216 |
| abstract_inverted_index.cloud | 81, 86, 199 |
| abstract_inverted_index.data. | 116 |
| abstract_inverted_index.delay | 189 |
| abstract_inverted_index.fog-, | 74 |
| abstract_inverted_index.life. | 164 |
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| abstract_inverted_index.meant | 185 |
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| abstract_inverted_index.these | 47 |
| abstract_inverted_index.using | 214 |
| abstract_inverted_index.(DNN-) | 178 |
| abstract_inverted_index.(IoT). | 17 |
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| abstract_inverted_index.delay, | 234 |
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| abstract_inverted_index.energy | 232, 265 |
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| abstract_inverted_index.hybrid | 198 |
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| abstract_inverted_index.level. | 237 |
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| abstract_inverted_index.sensor | 171 |
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| abstract_inverted_index.usage, | 266 |
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| abstract_inverted_index.attacks | 27 |
| abstract_inverted_index.cloud-, | 73 |
| abstract_inverted_index.cluster | 210 |
| abstract_inverted_index.concept | 247 |
| abstract_inverted_index.current | 256 |
| abstract_inverted_index.devices | 54, 144 |
| abstract_inverted_index.energy, | 155 |
| abstract_inverted_index.fitness | 157 |
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| abstract_inverted_index.results | 239 |
| abstract_inverted_index.safety, | 95 |
| abstract_inverted_index.sensing | 143 |
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| abstract_inverted_index.credence | 244 |
| abstract_inverted_index.delivers | 227 |
| abstract_inverted_index.fitness. | 173 |
| abstract_inverted_index.hazards. | 48 |
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| abstract_inverted_index.nodes’ | 172 |
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| abstract_inverted_index.security | 50, 127 |
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| abstract_inverted_index.superior | 253 |
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| abstract_inverted_index.(DNN-RSM) | 183 |
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| abstract_inverted_index.attention | 70 |
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| corresponding_author_ids | https://openalex.org/A5103195010, https://openalex.org/A5055484623 |
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