ارائه يك تكنيك بهبود‌يافته مشبك پوشش چندتايي با استفاده از مدل حسگري احتمالي براي شبكه‌هاي حسگر بي‌سيم
محورهای موضوعی : مهندسی برق و کامپیوترعبدالرضا واقفی 1 , مهدی ملامطلبی 2 *
1 - دانشگاه آزاد اسلامی قزوین
2 - دانشگاه آزاد اسلامی بوئین زهرا
کلید واژه: شبکه حسگر بی‌سیمپوشش منطقهرویکرد مشبکارزیابی پوشش تاییمدل حسگری احتمالی,
چکیده مقاله :
یکی از چالش‌های اساسی در شبکه‌های حسگر بی‌سیم، مسأله پوشش ناحیه تحت بررسی توسط یک یا چند گره است. به علت عمر محدود حسگرها و نیاز به داده‌های معتبر، کاربردهای نظارتی حساس نظیر شناسایی حریق، تشعشعات، نشت گاز، شناسایی نفوذ و غیره، پوشش منطقه تحت بررسی به وسیله چند گره حسگر انجام می‌گیرد که به آن پوشش تایی می‌گویند. اکثر تحقیقات گذشته در زمینه ارزیابی پوشش تایی بر اساس مدل حسگری باینری صورت گرفته است. تحقیق حاضر بر آن است که ارزیابی پوشش تایی را با رویکرد تقسیم‌بندی مشبک و از طریق مدل حسگری احتمالی و با هدف بهبود دقت و کاهش زمان ارزیابی پوشش انجام دهد. در پایان نیز روش پیشنهادی در محیط نرم‌افزار 2NS پیاده‌سازی و با روش‌های پیرامونی احتمالی و مشبک باینری، مقایسه شد. نتایج حاکی از بهبود دقت به میزان 14% و 24% نسبت به روش‌های مقایسه‌شده و کاهش زمان محاسبه ارزیابی پوشش تایی به میزان 7% نسبت به روش پیرامونی احتمالی است.
Coverage of an area, with one or multiple sensors, is one of the fundamental challenges in wireless sensor networks. Since a sensor life span is limited and reliable data is of great importance, sensitive applications like fire\leakage alarm systems, intrusion detection, etc. need multiple sensors to cover the region of interest, which is called K-coverage. Most of the studies that have been carried out on K-coverage evaluation have used binary sensing model. In this paper, we propose a grid-based K-coverage evaluation technique using probabilistic sensing model to increase evaluation accuracy and decrease evaluation time. The proposed technique is implemented using NS-2 simulator, and its results are compared to probabilistic perimeter-based and binary grid-based techniques. The results indicate that the proposed technique improved accuracy by 14% and 24% compared to the mentioned techniques respectively. It also shows 7% decrease in evaluation time compared to probabilistic perimeter-based technique.
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