Academic Journal
Resource-Aware Deep Learning Deployment for IoT–Fog Environments: A Novel BSIR and RAG-Enhanced Approach.
| Title: | Resource-Aware Deep Learning Deployment for IoT–Fog Environments: A Novel BSIR and RAG-Enhanced Approach. |
|---|---|
| Authors: | Atlam, Mostafa1 (AUTHOR) mostafa.atlam@el-eng.menofia.edu.eg, Attiya, Gamal1,2 (AUTHOR), Elrashidy, Mohamed1,2 (AUTHOR) |
| Source: | AI. Feb2026, Vol. 7 Issue 2, p44. 20p. |
| Subject Terms: | *Optimization algorithms, *Computer memory management, *Mathematical optimization, *Internet of things, *Edge computing, *Deep learning |
| Abstract: | The proliferation of Internet of Things (IoT) devices challenges deep learning (DL) deployment due to their limited computational power, while cloud offloading introduces high latency and network strain. Fog computing provides a viable middle ground. We present a resource-aware framework that intelligently partitions DL tasks between fog nodes and the cloud using a novel Binary Search-Inspired Recursive (BSIR) optimization algorithm for rapid, low-overhead decision-making. This is enhanced by a novel module that fine-tunes deployment by analyzing memory at a per-layer level. For true adaptability, a Retrieval-Augmented Generation (RAG) technique consults a knowledge base to dynamically select the best optimization strategy. Our experiments demonstrate dramatic improvements over established metaheuristics. The complete framework boosts memory utilization in fog environments to a remarkable 99%, a substantial leap from the 85.25% achieved by standard algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The enhancement module alone improves these traditional methods by over 13% without added computational cost. Our system consistently operates with a CPU footprint under 3% and makes decisions in fractions of a second, significantly outperforming recent methods in speed and resource efficiency. In contrast, recent DL methods may use 51% CPU and take over 90 s for the same task. This framework effectively reduces cloud dependency, offering a scalable solution for DL in the IoT landscape. [ABSTRACT FROM AUTHOR] |
| Database: | Academic Search Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: asx DbLabel: Academic Search Index An: 192097535 RelevancyScore: 1411 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1411.06896972656 |
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| Items | – Name: Title Label: Title Group: Ti Data: Resource-Aware Deep Learning Deployment for IoT–Fog Environments: A Novel BSIR and RAG-Enhanced Approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Atlam%2C+Mostafa%22">Atlam, Mostafa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mostafa.atlam@el-eng.menofia.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Attiya%2C+Gamal%22">Attiya, Gamal</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elrashidy%2C+Mohamed%22">Elrashidy, Mohamed</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22AI%22">AI</searchLink>. Feb2026, Vol. 7 Issue 2, p44. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+memory+management%22">Computer memory management</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br />*<searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The proliferation of Internet of Things (IoT) devices challenges deep learning (DL) deployment due to their limited computational power, while cloud offloading introduces high latency and network strain. Fog computing provides a viable middle ground. We present a resource-aware framework that intelligently partitions DL tasks between fog nodes and the cloud using a novel Binary Search-Inspired Recursive (BSIR) optimization algorithm for rapid, low-overhead decision-making. This is enhanced by a novel module that fine-tunes deployment by analyzing memory at a per-layer level. For true adaptability, a Retrieval-Augmented Generation (RAG) technique consults a knowledge base to dynamically select the best optimization strategy. Our experiments demonstrate dramatic improvements over established metaheuristics. The complete framework boosts memory utilization in fog environments to a remarkable 99%, a substantial leap from the 85.25% achieved by standard algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The enhancement module alone improves these traditional methods by over 13% without added computational cost. Our system consistently operates with a CPU footprint under 3% and makes decisions in fractions of a second, significantly outperforming recent methods in speed and resource efficiency. In contrast, recent DL methods may use 51% CPU and take over 90 s for the same task. This framework effectively reduces cloud dependency, offering a scalable solution for DL in the IoT landscape. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=192097535 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/ai7020044 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 44 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Computer memory management Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Resource-Aware Deep Learning Deployment for IoT–Fog Environments: A Novel BSIR and RAG-Enhanced Approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Atlam, Mostafa – PersonEntity: Name: NameFull: Attiya, Gamal – PersonEntity: Name: NameFull: Elrashidy, Mohamed IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 26732688 Numbering: – Type: volume Value: 7 – Type: issue Value: 2 Titles: – TitleFull: AI Type: main |
| ResultId | 1 |