افزايش كارايى محاسبات لبه به كمك رمزگذارى خودكار
محورهای موضوعی : هوش مصنوعی و رباتیک
1 - دانشجو
2 - استاد دانشگاه
کلید واژه: اینترنت اشیا, محاسبات لبه, ذخیره سازی داده ها, رمزگذارهای خودکار, پرسپترون های چندلایه,
چکیده مقاله :
گسترش سریع دستگاههای اینترنت اشیا (IoT) منجر به افزایش چشمگیر حجم دادههای تولیدی شده است که چالشهای قابل توجهی را برای محیطهای محاسباتی لبه ایجاد میکند. روشهای سنتی مبتنی بر رایانش ابری به دلیل محدودیتهای مربوط به تأخیر و حفظ حریم خصوصی، در پاسخگویی به نیازهای این فناوری با مشکلاتی مواجه هستند. بهمنظور غلبه بر این چالشها، پژوهشگران در جستجوی راهحلهای نوآورانه برای بهینهسازی ذخیرهسازی و پردازش دادهها در محیطهای محاسباتی لبه بودهاند. در این مطالعه، یک رویکرد ترکیبی معرفی شده است که از رمزگذارهای خودکار (AEs) و پرسپترونهای چندلایه (MLP) بهطور همزمان بهره میبرد تا کارایی پردازش دادهها در محاسبات لبه IoT را بهبود بخشد. این رویکرد با بهرهگیری از مزایای هر دو معماری، یک مدل قدرتمند برای مدیریت مؤثر دادههای IoT در مقیاس بزرگ ارائه میدهد. نتایج ارزیابی روش پیشنهادی نشان میدهد که این مدل با دستیابی به بالاترین دقت (0.88)، صحت (0.75)، فراخوانی (0.70) و امتیاز F1 (0.72)، در مقایسه با سایر روشها عملکرد بهتری دارد و میتواند بهعنوان یک راهکار کارآمد در بهینهسازی پردازش دادههای IoT مورد استفاده قرار گیرد.v
The rapid expansion of Internet of Things (IoT) devices has led to a significant increase in the volume of generated data, posing substantial challenges for edge computing environments. Traditional cloud-based computing methods struggle to meet the demands of this technology due to limitations related to latency and privacy. To overcome these challenges, researchers have been exploring innovative solutions to optimize data storage and processing in edge computing environments.
In this study, a hybrid approach is introduced that simultaneously leverages Autoencoders (AEs) and Multilayer Perceptrons (MLPs) to enhance data processing efficiency in IoT edge computing. By utilizing the advantages of both architectures, this approach provides a robust model for effectively managing large-scale IoT data. The evaluation results of the proposed method indicate that the model outperforms other approaches, achieving the highest accuracy (0.88), precision (0.75), recall (0.70), and F1-score (0.72). These findings suggest that the proposed solution can serve as an efficient approach for optimizing IoT data processing.
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