The paper introduces ENAMLE, an energy-aware technique to address data incompleteness due to sensor failures in IoT systems. It focuses on optimizing energy consumption while maintaining accuracy by using an ensemble of sub-models. The study evaluates SECOE's energy bottlenecks and proposes ENAMLE as a proactive solution. By dynamically adjusting the number of models based on missing data rates, ENAMLE effectively reduces energy consumption and optimizes throughput. Experimental studies demonstrate the efficiency of ENAMLE compared to SECOE and the base model across different datasets.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Yousef AlShe... at arxiv.org 03-18-2024
https://arxiv.org/pdf/2403.10371.pdfDeeper Inquiries