کاهش مصرف انرژی در شبکه‌های خودروی هوشمند با استفاده از مهاجرت پویای بار

شریفی, زهرا; حری, عباس; صمیمی دهکردی, لیلا

doi:10.47176/ECDJ.2025.1632

کاهش مصرف انرژی در شبکه‌های خودروی هوشمند با استفاده از مهاجرت پویای بار

نوع مقاله : مقاله پژوهشی

نویسندگان

زهرا شریفی ¹

عباس حری ²

لیلا صمیمی دهکردی ²

¹ کارشناسی ارشد، دانشگاه شهرکرد، شهرکرد، ایران

² استادیار، دانشگاه شهرکرد، شهرکرد، ایران

10.47176/ECDJ.2025.1632

چکیده

خودروهای هوشمند امروزی به شبکه‌های ارتباطی پیچیده متصل شده و حجم عظیمی از داده را مبادله می‌کنند. این امر منجر به افزایش مصرف انرژی در این شبکه‌ها شده است. در این پژوهش، روشی نوین برای بهینه‌سازی مصرف انرژی در شبکه‌های خودروهای هوشمند ارائه شده است. روش پیشنهادی بر پایه دو تکنیک متعادل‌سازی بار پویا طراحی شده است: نخست، تعیین آستانه‌های پویا بر اساس میانگین بار کاری ماشین‌های مجازی، دوم، پیش‌بینی بار آینده با استفاده از تحلیل رگرسیونی. نتایج شبیه‌سازی نشان می‌دهد که آستانه 10% و 20% بالاتر از میانگین بار کاری ماشین‌ها، مصرف انرژی شبکه را کاهش داده و نرخ تعادل بار را بهبود می‌بخشد. همچنین، مدل پیش‌بینی بار آینده، اختلاف میان بار پیش‌بینی‌شده و بار واقعی را تنها 5% نشان می‌دهد که بیانگر دقت بالای این روش است. این الگوریتم توانست مصرف انرژی را به طور قابل ملاحظه‌ای کاهش دهد و نرخ موفقیت مهاجرت ماشین‌های مجازی را بهبود ببخشد. این روش می‌تواند در آینده برای مدیریت بهینه منابع در شبکه‌های خودروهای هوشمند به کار گرفته شود و به کاهش هزینه‌های اجرایی و آلودگی محیط زیست کمک کند.

کلیدواژه‌ها

خودروهای هوشمند

اینترنت اشیا

مهاجرت پویای بار

بهینه‌سازی مصرف انرژی

شبکه‌های خودروهای متصل

موضوعات

مباحث دکترین، راهبردها و آینده پژوهی حوزه های مرتبط

عنوان مقاله English

Energy Efficiency in Smart Vehicular Networks Using Dynamic Load Migration

نویسندگان English

zahra Sharifi ¹

abbas horri ²

leila samimi ²

¹ Master's degree, Shahrekord University, Shahrekord, Iran

² Assistant Professor, Shahrekord University, Shahrekord, Iran

چکیده English

Modern smart vehicles are connected to complex communication networks and exchange a massive volume of data, leading to increased energy consumption in these networks. In this study, a novel method for optimizing energy consumption in smart vehicular networks is proposed. The proposed approach is based on two dynamic load balancing techniques: First, setting dynamic thresholds based on the average workload of virtual machines, second, predicting future workload using regression analysis. Simulation results demonstrate that using 10% and 20% higher than the average workload threshold reduces energy consumption and improves load balancing efficiency. Additionally, the workload prediction model shows only a 5% deviation between predicted and actual values, indicating high accuracy. This algorithm significantly reduces energy consumption and improves the success rate of virtual machine migrations. The proposed method can be further utilized for efficient resource management in smart vehicular networks, contributing to lower operational costs and environmental sustainability.

کلیدواژه‌ها English

Smart vehicles

Internet of Things (IoT)

Dynamic load migration

Energy consumption optimization

Vehicular networks

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