مسیریابی چندپخشی در شبکه‌های حسگر بی‌سیم مقیاس وسیع با استفاده از چارچوب یادگیری تقویتی توزیع شده
محورهای موضوعی : آمار
محمدصادق کردافشاری
1
(دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، گروه مهندسی کامپیوتر، تهران، ایران)
علی موقر
2
(استاد گروه مهندسی کامپیوتر، دانشگاه صنعتی شریف، تهران، ایران)
محمدرضا میبدی
3
(استاد دانشکده کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی امیرکبیر، تهران، ایران)
کلید واژه: Q-Learning, lifeTime, Reliability, Topology-independent,
چکیده مقاله :
یکی از چالش‌های مطرح در شبکه‌های حسگر بی‌سیم، مساله‌ی پیدا کردن مسیر مناسب برای ارسال همزمان بسته‌ی داده به چندین مقصد مختلف یا مسیریابی چندپخشی است به طوریکه مصرف انرژی در کل شبکه توزیع شود و بسته‌های داده با قابلیت اطمینان بالایی به مقصدهای مورد نظر مسیریابی شوند. با توجه به مزیت‌های فراوان استفاده از الگوریتم‌های یادگیری تقویتی، در این مقاله یک روش توزیع‌شده، انعطاف‌پذیر و مستقل از توپولوژی شبکه با استفاده از الگوریتم یادگیریQ برای مسیریابی چندپخشی ارائه شده است. در این الگوریتم هر گره حسگر مجهز به یک الگوریتم یادگیر است که بر اساس اطلاعات محلی تصمیمات مسیریابی خود را اتخاذ می-نماید و بسته‌ها را به مجموعه‌ای از سینک‌های آدرس چندپخشی ارسال می‌کند. الگوریتم یادگیر تلاش می‌کند که مسیرها با قابلیت اطمینان بالا، انرژی بیشتر و تراکم گره‌های بالاتر را برای مسیریابی انتخاب نماید. این الگوریتم در شبکه‌هایی وسیع که گره‌های حسگر اطلاعات کمی از یکدیگر دارد قابل استفاده است. شبیه‌سازی‌های انجام شده، روش پیشنهادی را از لحاظ درصد موفقیت مسیریابی بسته‌های داده، طول عمر شبکه و میزان مصرف حافظه را در دو حالت تراکم گره‌های بالا و افزایش تعداد سینکها مورد ارزیابی قرار داده است. نتایج به دست آمده کارآمدی روش پیشنهادی، به ویژه در شبکه‌هایی با تراکم بالا و درجه چندپخشی بالا را نشان می‌دهد.
Wireless Sensor Networks (WSNs) are consist of independent distributed sensors with storing, processing, sensing and communication capabilities to monitor physical or environmental conditions. There are number of challenges in WSNs because of limitation of battery power, communications, computation and storage space. In the recent years, computational intelligence approaches such as evolutionary algorithms and swarm intelligence are applied successfully to solve many problems in WSNs. Most important of these problems are data aggregation, energy-aware routing, duty cycle scheduling, security and localization. These problem are in form of distributed so distributed approaches are required to solve them. Reinforcement learning is one of the most widely used and most effective methods of computational intelligence. In this paper, we used the reinforcement learning to solve multicast Quality of Service (QoS) routing. The simulation results showed that reinforcement learning is a suitable approach to solve this problem. The algorithm is implemented easy, it has the great flexibility in topology changes and it leads to optimized results. Distributed reinforcement learning provides compatibility mechanisms that show the intelligence behavior in complicate and dynamic environment such as WSNs. Using reinforcement learning, sensors behave autonomously, independently and flexibly during topology and scenario changes.
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