بهبود پوشش هدف در شبکه‌های حسگر بصری با تنظیم میدان دید دوربین‌ها و زمان‌بندی مجموعه‌های پوششی از طریق تبرید شبیه‌سازی شده
محورهای موضوعی : مهندسی برق و کامپیوتر
بهروز شاهرخ‌زاده
1
(دانشگاه آزاد اسلامی، واحد قزوین)
مهدی دهقان
2
(دانشگاه صنعتي اميركبير)
محمدرضا شاهرخ‌زاده
3
(دانشگاه جامع علمی- کاربردی قزوين)
کلید واژه: پوشش هدف تبرید شبیه‌سازی شده شبکه‌های حسگر بصری طول عمر شبکه,
چکیده مقاله :
در سال‌های اخیر، مسأله «پوشش هدف» در شبکه‌های حسگر بصری که در آن دوربین‌ها داده‌های ویدئویی را جمع‌آوری می‌کنند مورد توجه قرار گرفته است. با توجه به محدودیت توان حسگرها، لازم است با استفاده بهینه از انرژی ذخیره‌شده، ضمن حفظ پوشش همه اهداف مستقر در محیط، طول عمر شبکه را نیز افزایش داد. در اینجا مسأله بیشینه‌سازی طول عمر شبکه با زمان‌بندی پوشش (MLCS) تعریف می‌شود که با تقسیم حسگرها به مجموعه‌های پوششی و سپس نوبت‌بندی خواب و بیدار آنها، در عین حفظ پوشش، مدت زمان عملیاتی شبکه افزایش می‌یابد. از طرفی، انتخاب بهترین میدان دید ممکن برای هر دوربین با توجه به موقعیت اهداف پیرامونی و با استفاده از قابلیت چرخش دوربین‌ها، ضمن کاهش اندازه فضای جواب، تأثیر مهمی در نزدیکی پاسخ مسأله به جواب بهینه دارد. برای حل مسأله، یک الگوریتم جدید مبتنی بر تبرید شبیه‌سازی شده (SA) با هدف یافتن تعداد مجموعه‌های پوششی بیشتر از حسگرهای دارای ذخیره انرژی بالاتر، پیشنهاد می‌شود. در روش پیشنهادی با ارائه یک تابع انرژی و تولید همسایگی جدید، ضمن پرهیز از تله نقطه بهینه محلی، توزیع متوازن انرژی حسگرها در سراسر شبکه تأمین می‌شود. نتایج شبیه‌سازی نشان می‌دهد که کارایی الگوریتم پیشنهادی نسبت به سایر روش‌ها از جمله روش‌های حریصانه، بهتر است.
In recent years, target coverage is one of the important problems in visual sensor networks. An efficient use of energy is required in order to increase the network lifetime, while covering all the targets. In this paper, we address the Maximum Lifetime with Coverage Scheduling (MLCS) problem that maximizes the network lifetime. We develop a simulated annealing (SA) algorithm that divides the sensors’ Field-of-View (FoV) to a number of cover sets that can cover all the targets and then applies a sleep-wake scheduling algorithm. On the other hand, we have to identify the best possible FoV of sensors according to the targets’ location using rotating cameras, to reduce the solution space and find a near-optimal solution. It also provides the balanced distribution of energy consumption by introducing a new energy and neighbor generating function as well as escaping from local optima. Finally, we conduct some simulation experiments to evaluate the performance of our proposed method by comparing with well-known solutions in the literature such as greedy algorithms.
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