بهینه‌سازی طرح تطبیقی شبکه حسگر بی‌سیم با استفاده از الگوریتم جستجوی گرانشی باینری کوانتومی
محورهای موضوعی : مهندسی برق و کامپیوتر
مینا میرحسینی
1
(مجتمع آموزش عالی بم)
فاطمه بارانی
2
(مجتمع آموزش عالی بم)
حسین نظام‌آبادی‌پور
3
(دانشگاه شهید باهنر کرمان)
کلید واژه: الگوریتم جستجوی گرانشی الگوریتم جستجوی گرانشی باینری کوانتومی شبکه حسگر بی‌سیم, کشاورزی نظارت‌شده,
چکیده مقاله :
افزایش طول عمر، کارایی و کاهش مصرف انرژی در شبکه‌های حسگر بی‌سیم یک مسئله چندهدفه است که یکی از موضوعات چالش‌برانگیز در تحقیقات اخیر شده است. در این مقاله به منظور افزایش کارایی و طول عمر شبکه‌های حسگر بی‌سیم، با استفاده از الگوریتم جستجوی گرانشی باینری کوانتومی روشی پیشنهاد شده که علاوه بر کمینه‌کردن مصرف انرژی، محدودیت‌های ارتباطی شبکه و نیازمندی‌های کاربرد خاص آن نیز برآورده می‌گردد. این الگوریتم روی یک شبکه حسگر بی‌سیم در کاربرد کشاورزی و به منظور نظارت دقیق و اصولی شرایط محیطی استفاده شده است. نتیجه به کارگیری این الگوریتم روی شبکه حسگر بی‌سیم، یک طرح بهینه خواهد بود که در آن حالت عملیاتی هر حسگر شامل سرگروه، حسگر فعال با محدوده حسگری بلند، حسگر فعال با محدوده حسگری کوتاه و غیر فعال را با توجه به محدودیت‌های مسئله مشخص می‌نماید. نتایج شبیه‌سازی نشان می‌دهد که این الگوریتم در شبکه حسگر بی‌سیم در مقایسه با الگوریتم وراثتی و الگوریتم ازدحام جمعیت نتایج بهتری را ارائه می‌دهد و متعاقباً قادر است که طول عمر شبکه را نسبت به دو الگوریتم دیگر به نحو مطلوب‌تری افزایش دهد.
In this paper, the binary quantum-inspired gravitational search algorithm is adapted to dynamically optimize the design of a wireless sensor network towards improving energy consumption and extending the lifetime of the network, so that the application-specific requirements and communication constraints are fulfilled. The proposed approach is applied on a wireless sensor network used in the application of precise agriculture to monitor environmental conditions. This algorithm would present an optimal design detecting operational mode of each sensor including cluster head, high signal range, low signal range and inactive modes taking into consideration the constraints of the network. The simulation results indicate the most performance of the proposed method in comparison with binary genetic algorithm and particle swarm optimization.
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