فصلنامه علمی کارافن

فصلنامه علمی کارافن

ارائه روش مسیریابی در اینترنت اشیاء برای توزیع بار بهتر با استفاده از الگوریتم بهینه‌سازی گرگ خاکستری بهبودیافته

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان
1 گروه مهندسی برق، دانشگاه فنی و حرفه‌ای، تهران، ایران.
2 دانشجوی کارشناسی، گروه مهندسی برق، دانشگاه فنی و حرفه‌ای، تهران، ایران.
چکیده
اینترنت اشیاء شبکه‌ای است از تجهیزات متنوع که دارای ویژگی‌ها و محدودیت‌های مختلفی هستند، یکی از ویژگی‌های شبکه‌ی اینترنت اشیاء ناهمگن بودن وسایل و پروتکل‌های ارتباطی آن است؛ بنابراین نیازمند الگوریتم مسیریابی بهینه که مصرف انرژی را کاهش دهد می‌باشد. توزیع بار در شبکه یکی از روش‌هایی است که مصرف انرژی را به‌خوبی بهینه می‌نماید. در این مقاله با استفاده از الگوریتم بهینه‌سازی گرگ خاکستری بهبودیافته روشی برای انتخاب سرخوشه‌ها ارائه شده است که هدف آن توزیع بار مناسب در شبکه و کاهش مصرف انرژی می‌باشد. در این روش فرایند انتخاب سرخوشه‌ها با استفاده از معیارهای مختلفی انجام شده است تا توزیع بار بهتر و مصرف انرژی کم‌تر شود. روش بهبودیافته همگرایی بهتری نسبت به روش اصلی بهینه‌سازی گرگ خاکستری دارد. روش پیشنهادی توسط MATLAB 2019a پیاده‌سازی شد. شبیه‌سازی‌های انجام شده و نتایج به‌دست‌آمده با الگوریتم‌های بهینه‌سازی ازدحام ذرات، بهینه‌سازی گرگ خاکستری، زنبورعسل مصنوعی و کرم شب‌تاب مقایسه شد در پارامترهای تأخیر انتها به انتها، همگرایی، مصرف انرژی نسبت به سایر روش‌ها عملکرد بهتری را نشان داد. می‌توان نتیجه گرفت که تعادل بار بهتری روی شبکه صورت‌گرفته است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Providing a Routing Method in Internet of Things for Better Load Distribution Using the Improved Gray Wolf Optimization Algorithm

نویسندگان English

Kiomars Sabzevari 1
Amir Solimani 2
1 Electrical Engineering Faculty, Technical and Vocational University (TVU), Tehran, Iran.
2 Student, Electrical Engineering Faculty, Technical and Vocational University (TVU), Tehran, Iran.
چکیده English

The The Internet of Things network consists of many devices with various characteristics and constraints. One of the major properties of IoT is that it has heterogeneous nodes and communication protocols. Hence, it is a required routing method that decreases the energy consumption of the network. This article proposed a cluster head selection method using improved grey wolf optimization. The goal of the proposed algorithm was to improve load balancing and decrease power consumption. This process method of cluster head selection uses many metrics to improve load balancing and energy consumption. This method has more convergence than traditional grey wolf optimization. The proposed method was implemented by MATLAB 2019a and many simulations were run to compare with three other metaheuristic algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), improved artificial bee colony (IABC), and glowworm swarm optimization (GSO). The results of the simulations show that the proposed method improved end-to-end delay, convergence, and energy consumption parameters. It can be concluded that load balancing on IoT networks improved.

کلیدواژه‌ها English

Internet Of Things
Grey Wolf Optimization
Routing
Load Balancing
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دوره 21، شماره 1 - شماره پیاپی 66
فنی و مهندسی
بهار 1403
صفحه 109-131

  • تاریخ دریافت 19 آبان 1402
  • تاریخ بازنگری 18 بهمن 1402
  • تاریخ پذیرش 17 فروردین 1403