Resource-Aware Deep Learning Deployment for IoT–Fog Environments: A Novel BSIR and RAG-Enhanced Approach
جاري التحميل...
التاريخ
عنوان الدورية
ردمد الدورية
عنوان المجلد
الناشر
AI, MDPI
خلاصة
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.