نمونه برداری حسگر فشرده در شبکه

•   آرشیو : نسخه تابستان 1404
•   کد پذیرش : 12426
•   موضوع : سایر شاخه ها
• نویسنده/گان : | محسن نصیری
•   زبان : فارسی
•   نوع مقاله : پژوهشی
•   چکیده مقاله به فارسی : حسگری فشرده در شبکه حسگر بی سیم اخیراً به منظور فشرده‌سازی/ بازیابی سیگنال و جمع‌آوری داده مورد استفاده قرارگرفته است. دلیل محبوبیت روش فوق در شبکه حسگر بی سیم را می‌توان مربوط به چالش‌های این تکنولوژی دانست، زیرا تئوری حسگری فشرده با نرخ اندک نمونه‌برداری خود سبب کاهش توان مصرفی و کاهش نرخ انتقال داده می‌شود که این امر موجب کاهش انرژی مصرفی نودها درنتیجه افزایش طول عمر شبکه می‌شود، این تئوری یک الگوی جدید نمونه‌برداری است که نمونه‌های سیگنال در یک روش بسیار کارآمدتر از تئوری نایکوئیست ایجاد می‌شوند، حسگری فشرده به‌تازگی به لطف استفاده از تنکی سیگنال بسیار موردتوجه قرا گفته است. در سال‌های اخیر حل مسائل مربوط به ابعاد- بالا با بهره بردن از ساختارهای ابعاد- کوچک پیشرفت چشم‌گیر داشته است. تنکی را می‌توان ساده‌ترین مدل برای ساختار با ابعاد- کوچک دانست، تنکی بر این پایه استوار است که اشیا (می‌تواند سیگنال باشد) موردعلاقه را می‌توان به‌صورت یک ترکیب خطیاز تعداد کوچکی از توابع ابتدایی نمایش داد که فرض شده است آن‌ها مطلق به مجموعه بزرگ‌تر یا یک فرهنگ لغت یا احتمالاً تابعی بزرگ‌تر می‌باشند.
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•   کلمات کلیدی به فارسی : نمونه برداری، شبکه بی سیم، فشردگی، امنیت شبکه
•   چکیده مقاله به انگلیسی : Compact sensing in wireless sensor networks has recently been used for signal compression/recovery and data collection. The reason for the popularity of the above method in wireless sensor networks can be attributed to the challenges of this technology, because the compact sensing theory with its low sampling rate reduces power consumption and data transfer rate, which reduces the energy consumption of nodes, resulting in an increase in the network lifetime. This theory is a new sampling paradigm in which signal samples are generated in a much more efficient way than the Nyquist theory. Compact sensing has recently gained much attention thanks to the use of signal sparsity. In recent years, solving high-dimensional problems using small-dimensional structures has made significant progress. Sparsity can be considered the simplest model for low-dimensional structures. Sparsity is based on the premise that the objects (which can be signals) of interest can be represented as a linear combination of a small number of elementary functions that are assumed to be absolute to a larger set or dictionary or possibly a larger function.
•   کلمات کلیدی به انگلیسی : Sampling, Wireless Network, Compression, Network Security
•  صفحات : 18-38
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