Resource-Aware Deep Learning Deployment for IoT–Fog Environments: A Novel BSIR and RAG-Enhanced Approach
| dc.contributor.author | Dr. Mostafa Atlam | |
| dc.contributor.author | Mr. Gamal Attiya | |
| dc.contributor.author | Dr. Mohamed Elrashidy | |
| dc.date.accessioned | 2026-05-19T11:03:43Z | |
| dc.date.issued | 2026-01-30 | |
| dc.description.abstract | 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. internet of things - deep learning - fog computing - cloud computing - retrieval augmented generation - heuristic algorithms | |
| dc.identifier.uri | https://research.arabeast.edu.sa/handle/123456789/1092 | |
| dc.language.iso | en | |
| dc.publisher | AI, MDPI Switzerland | |
| dc.title | Resource-Aware Deep Learning Deployment for IoT–Fog Environments: A Novel BSIR and RAG-Enhanced Approach | |
| dc.type | Article |