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بهبود نرخ پوشش و کاهش هزینه پایش در پایش جمعی سیار با استفاده از الگوریتم بهینه‌سازی جنگل آشوبگون | ||
| پدافند الکترونیکی و سایبری | ||
| مقاله 8، دوره 11، شماره 3 - شماره پیاپی 43، آبان 1402، صفحه 77-88 اصل مقاله (723.41 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| طاهره متدین1؛ مهدی یعقوبی* 2؛ مریم خیرآبادی3 | ||
| 1دانشجوی دکتری،گروه مهندسی کامپیوتر، واحد نیشابور، دانشگاه آزاد اسلامی، نیشابور، ایران | ||
| 2دانشیار،گروه کامپیوتر، دانشگاه ازاد اسلامی، واحد مشهد،مشهد، ایران. | ||
| 3استادیار،گروه کامپیوتر، دانشگاه آزاد اسلامی، واحد نیشابور، نیشابور، ایران | ||
| تاریخ دریافت: 06 فروردین 1402، تاریخ بازنگری: 04 مرداد 1402، تاریخ پذیرش: 29 مرداد 1402 | ||
| چکیده | ||
| در اینترنت اشیا حجم انبوهی از داده‌های مختلف تولید می‌شود که برای پردازش به مرکز داده ارسال می‌گردد. برای افزایش توان پردازشی، اینترنت اشیا مبتنی بر رایانش مه پیشنهاد شده است. در اینترنت اشیا مبتنی بر مه کاربران با تجهیزات هوشمند (مانند گوشی‌های همراه) وظایف را پایش کرده و در انجام آن‌ها مشارکت می‌کنند. به این فرآیند پایش جمعی سیار گفته می‌شود. در پایش جمعی سیار، اختصاص پاداش(هزینه) بدون برنامه‌ریزی به کاربران، می‌تواند هزینه‌های بستر را افزایش دهد و قابلیت‌های برنامه‌های کاربردی را تهدید کند. بنابراین تعیین سیاست پاداش منطقی برای کاربران به‌منظور کاهش هزینه‌های بستر به صورتی که نرخ پوشش شبکه نیز بیشنه باشد از چالش‌های مهم در این فناوری است. افزایش نرخ پوشش شبکه و کاهش هزینه‌های بستر را می‌توان در قالب یک مساله بهینه‌سازی مطرح کرد. اما ارائه الگوریتمی که در بهینه‌های محلی کمتر گرفتار شود و بتواند همواره نتایج مطلوبی ارائه دهد خود یک چالش دیگر است. در این مقاله تلاش شده است با بهره‌گیری از تئوری آشوب، و الگوریتم بهینه‌سازی جنگل رویکردی جدید و کارآمد برای پایش جمعی سیار ارائه شود. روش پیشنهادی در نرم‌افزار MATLB پیاده‌سازی شده و تجزیه‌وتحلیل یافته‌ها نشان می‌دهد که روش پیشنهادی توانسته است نرخ پوشش شبکه و هزینه پایش را نسبت به طرح های مشابه بهینه کند. | ||
| کلیدواژه‌ها | ||
| اینترنت اشیا؛ پایش جمعی سیار؛ الگوریتم بهینه‌سازی جنگل؛ تئوری آشوب | ||
| عنوان مقاله [English] | ||
| Improving mobile mass monitoring in the IoT environment based on Chaotic Fog Algorithm | ||
| نویسندگان [English] | ||
| Tahere Motedaien1؛ Mahdi Yaghoobi2؛ Maryam kheirabadi3 | ||
| 1PhD student. Computer Engineering Dep., Neyshabour Branch, Islamic Azad University, Neyshabour, Iran | ||
| 2Associate Professor, Computer Department, Islamic Azad University, Mashhad Branch, Mashhad, Iran. | ||
| 3Assistant Professor, Computer Department, Islamic Azad University, Neyshabor Branch, Neyshabor, Iran | ||
| چکیده [English] | ||
| In the IoT-based IoT environment, users can monitor tasks in the network environment and participate in the data collection process by smart devices. Users monitor their environment data in the form of fog computing during this process, also called mobile mass monitoring, and service providers are required to pay user rewards. But rewards should not be such as to increase platform costs. At the same time, maximizing the maximization rate is one of the main goals of service providers. Increasing network coverage rates and reducing platform costs can be considered as an optimization problem. But providing an algorithm that is less involved in local optimizations and can always provide good results is a challenge in itself. This article is tried to present an efficient approach based on the improved forest optimization algorithm using chaos theory and fuzzy parameter adjustment to reduce platform costs and maximize mobile mass monitoring coverage rate. The proposed method is implemented in MATLAB software and the analysis of the findings shows that the proposed method can optimize the network coverage rate by 31% (average) and the monitoring cost by 11% (average) compared to the CMST plan. | ||
| کلیدواژه‌ها [English] | ||
| Internet of Things, mobile mass monitoring, Forest Optimization Algorithm, Chaos Theory, Fuzzy System | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 162 تعداد دریافت فایل اصل مقاله: 318 |
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