Resource-Aware Deep Learning Deployment for IoT–Fog Environments: A Novel BSIR and RAG-Enhanced Approach
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الناشر
AI, MDPI Switzerland
خلاصة
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