Bayesian Input Compression for Edge Intelligence in Industry 4.0 Article Swipe
Handuo Zhang
,
Jun Guo
,
Xiaoxiao Wang
,
Bin Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14173416
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14173416
In Industry 4.0 environments, edge intelligence plays a critical role in enabling real-time analytics and autonomous decision-making by integrating artificial intelligence (AI) with edge computing. However, deploying deep neural networks (DNNs) on resource-constrained edge devices remains challenging due to limited computational capacity and strict latency requirements. While conventional methods primarily focus on structural model compression, we propose an adaptive input-centric approach that reduces computational overhead by pruning redundant features prior to inference. A Bayesian network is employed to quantify the influence of each input feature on the model output, enabling efficient input reduction without modifying the model architecture. A bidirectional chain structure facilitates robust feature ranking, and an automated algorithm optimizes input selection to meet predefined constraints on model accuracy and size. Experimental results demonstrate that the proposed method significantly reduces memory usage and computation cost while maintaining competitive performance, making it highly suitable for real-time edge intelligence in industrial settings.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics14173416
- https://www.mdpi.com/2079-9292/14/17/3416/pdf?version=1756298575
- OA Status
- gold
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413796109
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4413796109Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics14173416Digital Object Identifier
- Title
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Bayesian Input Compression for Edge Intelligence in Industry 4.0Work title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-27Full publication date if available
- Authors
-
Handuo Zhang, Jun Guo, Xiaoxiao Wang, Bin ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics14173416Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/14/17/3416/pdf?version=1756298575Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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-
https://www.mdpi.com/2079-9292/14/17/3416/pdf?version=1756298575Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Edge computing, Pruning, Edge device, Analytics, Enhanced Data Rates for GSM Evolution, Machine learning, Bayesian network, Feature selection, Computation, Artificial neural network, Inference, Data mining, Cloud computing, Algorithm, Operating system, Biology, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4396877941, https://openalex.org/W4388079247, https://openalex.org/W3004543888, https://openalex.org/W4390822412, https://openalex.org/W3034797615, https://openalex.org/W3033428961, https://openalex.org/W4402137675, https://openalex.org/W4205952419, https://openalex.org/W4402654353, https://openalex.org/W2963125010, https://openalex.org/W2883780447, https://openalex.org/W2962772649, https://openalex.org/W4386158963, https://openalex.org/W4394008356, https://openalex.org/W2989658562, https://openalex.org/W3004633656, https://openalex.org/W3007902335, https://openalex.org/W4406754702, https://openalex.org/W4402352824, https://openalex.org/W3201412947, https://openalex.org/W4308028076, https://openalex.org/W3043734689, https://openalex.org/W3007897323, https://openalex.org/W4386518671, https://openalex.org/W2169466806, https://openalex.org/W2135099885, https://openalex.org/W2114623221, https://openalex.org/W4390604386, https://openalex.org/W3111967092, https://openalex.org/W3181129449, https://openalex.org/W2809251854, https://openalex.org/W4312796067, https://openalex.org/W3010919326, https://openalex.org/W3195252999, https://openalex.org/W2919115771, https://openalex.org/W4226340826, https://openalex.org/W4392152832, https://openalex.org/W2123649031, https://openalex.org/W2104636679, https://openalex.org/W2963000224, https://openalex.org/W2963981420, https://openalex.org/W3159460870, https://openalex.org/W3105131457, https://openalex.org/W4387799560, https://openalex.org/W2134797427, https://openalex.org/W4383604976 |
| referenced_works_count | 46 |
| abstract_inverted_index.A | 72, 98 |
| abstract_inverted_index.a | 7 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.an | 57, 107 |
| abstract_inverted_index.by | 17, 65 |
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| abstract_inverted_index.it | 141 |
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| abstract_inverted_index.we | 55 |
| abstract_inverted_index.4.0 | 2 |
| abstract_inverted_index.and | 14, 42, 106, 120, 133 |
| abstract_inverted_index.due | 37 |
| abstract_inverted_index.for | 144 |
| abstract_inverted_index.the | 79, 86, 95, 126 |
| abstract_inverted_index.(AI) | 21 |
| abstract_inverted_index.cost | 135 |
| abstract_inverted_index.deep | 27 |
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| abstract_inverted_index.edge | 4, 23, 33, 146 |
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| abstract_inverted_index.focus | 50 |
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| abstract_inverted_index.model | 53, 87, 96, 118 |
| abstract_inverted_index.plays | 6 |
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| abstract_inverted_index.size. | 121 |
| abstract_inverted_index.usage | 132 |
| abstract_inverted_index.while | 136 |
| abstract_inverted_index.(DNNs) | 30 |
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| abstract_inverted_index.making | 140 |
| abstract_inverted_index.memory | 131 |
| abstract_inverted_index.method | 128 |
| abstract_inverted_index.neural | 28 |
| abstract_inverted_index.robust | 103 |
| abstract_inverted_index.strict | 43 |
| abstract_inverted_index.devices | 34 |
| abstract_inverted_index.feature | 84, 104 |
| abstract_inverted_index.latency | 44 |
| abstract_inverted_index.limited | 39 |
| abstract_inverted_index.methods | 48 |
| abstract_inverted_index.network | 74 |
| abstract_inverted_index.output, | 88 |
| abstract_inverted_index.propose | 56 |
| abstract_inverted_index.pruning | 66 |
| abstract_inverted_index.reduces | 62, 130 |
| abstract_inverted_index.remains | 35 |
| abstract_inverted_index.results | 123 |
| abstract_inverted_index.without | 93 |
| abstract_inverted_index.Bayesian | 73 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.Industry | 1 |
| abstract_inverted_index.accuracy | 119 |
| abstract_inverted_index.adaptive | 58 |
| abstract_inverted_index.approach | 60 |
| abstract_inverted_index.capacity | 41 |
| abstract_inverted_index.critical | 8 |
| abstract_inverted_index.employed | 76 |
| abstract_inverted_index.enabling | 11, 89 |
| abstract_inverted_index.features | 68 |
| abstract_inverted_index.networks | 29 |
| abstract_inverted_index.overhead | 64 |
| abstract_inverted_index.proposed | 127 |
| abstract_inverted_index.quantify | 78 |
| abstract_inverted_index.ranking, | 105 |
| abstract_inverted_index.suitable | 143 |
| abstract_inverted_index.algorithm | 109 |
| abstract_inverted_index.analytics | 13 |
| abstract_inverted_index.automated | 108 |
| abstract_inverted_index.deploying | 26 |
| abstract_inverted_index.efficient | 90 |
| abstract_inverted_index.influence | 80 |
| abstract_inverted_index.modifying | 94 |
| abstract_inverted_index.optimizes | 110 |
| abstract_inverted_index.primarily | 49 |
| abstract_inverted_index.real-time | 12, 145 |
| abstract_inverted_index.reduction | 92 |
| abstract_inverted_index.redundant | 67 |
| abstract_inverted_index.selection | 112 |
| abstract_inverted_index.settings. | 150 |
| abstract_inverted_index.structure | 101 |
| abstract_inverted_index.artificial | 19 |
| abstract_inverted_index.autonomous | 15 |
| abstract_inverted_index.computing. | 24 |
| abstract_inverted_index.industrial | 149 |
| abstract_inverted_index.inference. | 71 |
| abstract_inverted_index.predefined | 115 |
| abstract_inverted_index.structural | 52 |
| abstract_inverted_index.challenging | 36 |
| abstract_inverted_index.competitive | 138 |
| abstract_inverted_index.computation | 134 |
| abstract_inverted_index.constraints | 116 |
| abstract_inverted_index.demonstrate | 124 |
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| abstract_inverted_index.integrating | 18 |
| abstract_inverted_index.maintaining | 137 |
| abstract_inverted_index.Experimental | 122 |
| abstract_inverted_index.compression, | 54 |
| abstract_inverted_index.conventional | 47 |
| abstract_inverted_index.intelligence | 5, 20, 147 |
| abstract_inverted_index.performance, | 139 |
| abstract_inverted_index.architecture. | 97 |
| abstract_inverted_index.bidirectional | 99 |
| abstract_inverted_index.computational | 40, 63 |
| abstract_inverted_index.environments, | 3 |
| abstract_inverted_index.input-centric | 59 |
| abstract_inverted_index.requirements. | 45 |
| abstract_inverted_index.significantly | 129 |
| abstract_inverted_index.decision-making | 16 |
| abstract_inverted_index.resource-constrained | 32 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100445470 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I9224756 |
| citation_normalized_percentile.value | 0.47947331 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |