بهبود روش نمونه برداری تجمیع داده مبتنی بر سنجش فشرده و یادگیری لغت نامه در شبکه حسگر بی‌سیم به کمک نظریه اطلاعات

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی مکانیک، برق و کامپیوتر- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

2 پژوهشگاه ارتباطات و فن آوری اطلاعات، تهران، ایران

چکیده

در دهه اخیر با هدف کاهش هزینه‌های نظارت محیطی، فرایند تجمیع داده مبتنی بر روش مشترک سنجش فشرده و یادگیری لغت­نامه در شبکه‌های حسگر بی‌سیم مورد توجه قرار گرفته‌است. در این مقاله یک طرح نمونه­برداری قطعی و غیرتصادفی برای استفاده در این روش تجمیع داده ارائه شده‌است. این طرح مبتنی بر برآورد کمیت اطلاعات متقابل داده حسگرها است که با نمونه­برداری از تمام آنها در بخش کوتاهی از دوره جمع‌آوری داده به­نام مرحله آموزش به­دست می‌آید. در مرحله بعدی و اصلی دوره جمع‌آوری داده گره‌هایی نمونه­برداری می‌شوند که بیشترین اطلاعات را درباره گره‌های نمونه­برداری نشده در اختیار بگذارند. نتایج شبیه­سازی‌ها با سیگنال­های واقعی نشان می‌دهد که حتی زمانی­که تعداد حسگرهای نمونه بردار تنها شامل 25 درصد از کل گره‌های شبکه است می‌توان به‌طور متوسط به بیش از 12 درصد صرفه­جویی در مصرف انرژی نسبت به روش نمونه­برداری مرجع دست یافت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Improved Sensor Sampling Method for the Joint Dictionary Learning and Compressive Data Gathering in WSNs with the Aid of Information Theory

نویسندگان [English]

  • Gholamreza Imanian 1
  • Mohammad Ali Pourmina 1
  • Ahmad Salahi 2
1 Department of Mechanical, Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Iran Telecommunication Research Center, Tehran, Iran
چکیده [English]

In the last decade, to reduce the costs of environmental monitoring, the data aggregation based on the joint dictionary learning and compressive sensing technique in wireless sensor networks has been considered. In this article, a deterministic and non-random sampling design for use in this data aggregation method is presented. This method is based on estimating the amount of mutual information of sensor data and is obtained by sampling all of them in a short part of the data collection round named the training phase. In the next and main stage of the data collection period, only the nodes that provide the most information about the non-sampled nodes are scheduled to sample. Simulation results for real signals show that when the number of sampling sensors comprises still about 25% of the total network nodes, average energy savings of more than 12% can be achieved over a reference sampling method.

کلیدواژه‌ها [English]

  • Compressive Sensing
  • Data aggregation
  • dictionary learning
  • Environmental Monitoring
  • Wireless Sensor Network
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