| دانلود رایگان مقاله انگلیسی + خرید ترجمه فارسی
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| عنوان فارسی مقاله: |
استقرار مبتنی بر P.S.O دودویی گسسته جدید شبکه های حسگر بی سیم |
| عنوان انگلیسی مقاله: |
Novel Discrete Binary P.S.O based deployment of Wireless Sensor Network |
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| مشخصات مقاله انگلیسی (PDF) | |
| سال انتشار مقاله | 2014 |
| تعداد صفحات مقاله انگلیسی | 5 صفحه با فرمت pdf |
| رشته های مرتبط با این مقاله | مهندسی کامپیوتر، مهندسی برق و مهندسی فناوری اطلاعات و ارتباطات |
| گرایش های مرتبط با این مقاله | نرم افزار، مهندسی الگوریتم ها و محاسبات، معماری کامپیوتر، مهندسی فناوری اطلاعات، برق کنترل، برق مخابرات و برق الکترونیک |
| مجله مربوطه | مجله بین المللی تحقیقات پیشرفته در کامپیوتر و مهندسی ارتباطات(International Journal of Advanced Research in Computer and Communication Engineering) |
| دانشگاه تهیه کننده | دپارتمان الکترونیک و ارتباط، هند |
| کلمات کلیدی این مقاله | PSO دودویی گسسته جدید، استقرار حسگر توزیع شده، پوشش، استقرار حسگر، شبکه های حسگر بی سیم |
| رفرنس | دارد |
| نشریه | Ijarcce |
| مشخصات و وضعیت ترجمه فارسی این مقاله (Word) | |
| تعداد صفحات ترجمه تایپ شده با فرمت ورد با قابلیت ویرایش و فونت 14 B Nazanin | 15 صفحه |
| ترجمه عناوین تصاویر | ترجمه شده است |
| ترجمه متون داخل تصاویر | ترجمه نشده است |
| درج تصاویر در فایل ترجمه | درج شده است |
| درج فرمولها و محاسبات در فایل ترجمه به صورت عکس | درج شده است |
- فهرست مطالب:
چکیده
۱ مقدمه
۲ پیشینه بهینه سازی ازدحام ذرات
A الگوریتم PSO
Bبهینه سازی ازدحام ذرات گسسته
۳ تعریف مسئله
۴ روش تحقیق
۵ شبیه سازی و نتایج
۶ نتیجه گیری
- بخشی از ترجمه:
از پلات های مختلف پس از اجرای تکنیک بهینه سازی ازدحام ذرات دودویی گسسته جدید نتایج زیر بدست آمده است:
1. این تکنیک ساده، سریع ، از لحاظ محاسباتی کارآمد و پیاده سازی آن راحت می باشد وقابلیت استقرار بسیار کارآمد حسگرها با هدف به حداکثر رساندن ناحیه تحت پوشش و به حداقل رساندن هزینه های شبکه را دارد.
2. برای پوشش کل منطقه مربوطه به حداقل تعداد حسگرها نیاز بوده و درکمتر از تعداد مقرر شده، پوشش کل فیلد کاری به شکلی کارآمد امکان پذیر نمی باشد.
3. با افزایش چگالی گره های گرید WSN، تعداد حسگرهای بکاررفته نیز افزایش و ناحیه مربوطه به شکلی کارآمد پوشش داده می شود.
- بخشی از مقاله انگلیسی:
INTRODUCTION Wireless Sensor Networks (WSNs) have gained tremendous importance in recent years because of its potential use in a wide range of applications such as target tracking, surveillance and security management etc. Due to the increasing potential of WSN, along with its unique characteristics such as easy accessibility to difficult terrain, efficient transmission and user‟s friendly interface etc., there will be a significant spur of research in coming years in the field of developing network protocols specifically tailored for sensor networks. Wireless Sensor Networks consist of a number of battery powered sensor nodes, endowed with physical sensing capabilities, limited processing, memory, and short-range radio communication [2]. In these networks a large set of nodes may be distributed over a wide geographical area, indoor or outdoor enabling a number of sensing and monitoring services in the areas of vital importance such as industrial production monitoring and environmental monitoring. These nodes collectively form a network and forward the gathered information to a data sink or gateway [3]. In general, a sensor node includes a sensing device for data acquisition from the physical environment, a processing subsystem for local data processing and storage, and a wireless communication module. Additionally, a power source supplies the energy needed by the device to perform all the programmed tasks. The power source in most of the cases is battery having limited life. Therefore, maximizing the network lifetime is also an important challenge and should be tackled either at the planning stage or by means of a recursive optimization of the network lifetime under minimal coverage constraint. Thus, in order to support planning and deployment as well as to enable testing of new protocols and applications, simulation platforms have been extended to include simulation frameworks for WSN. While studying the coverage area, most of the researchers assumed that the sensor nodes are static[4]. However, these days new type of mobile sensor nodes are used which have the limited ability to relocate themselves after their deployment. Different algorithms [5], [6], [7], [8] have been proposed by researchers to relocate the sensor nodes to optimize the coverage and time. The method proposed by Howard et al. [5] uses iterative sequences for determining location each sensor node needs to move to in order to optimize the coverage. Traditional analytical optimization techniques require a lot of computational efforts, which grows exponentially as the problem size increases. An optimization method that requires moderate memory and computational resources and yet produces good results is desirable for implementation on an individual sensor node. Optimization methods inspired by biological activities are computationally efficient alternatives to analytical methods. Particle swarm optimization (PSO) is a popular multidimensional optimization technique [9]. Ease of implementation, high quality solutions, computational efficiency and speed of convergence are strengths of PSO[10]. Keeping in mind the deployment needs of sensors in wireless network and the characteristics of PSO a new algorithm has been proposed to deploy wireless sensor network using novel discrete binary particle swarm optimization technique. The algorithm is implemented and illustrated using MATLAB for the purpose. II. BACKGROUND OF PARTICLE SWARM OPTIMIZATION PSO is inspired by observing the bird flocking or fish school [10]. Scientists found that the synchrony of flocking behavior was through maintaining optimal distances between individual members and their neighbours. Thus, velocity plays an important role of adjusting each other for the optimal distance. Furthermore, the scientists simulated the scenario in which birds search for food and observed their social behavior [12]. They perceived that in order to find food the individual members determined their velocities by two factors, their own best experience of past and the best experience of all other members [10]. This is similar to the human behavior in making decision where people consider their own best past experience and the best experience of how the other people around them have performed [11].are required. According to the above concept, Kennedy and Eberhart [10] developed the so-called PSO for optimization of continuous nonlinear functions in 1995. In PSO algorithm, each answer to the problem is considered as a bird in the search space which is called a particle. Each particle has its own fitness determined by the fitness function. A bird which is close to food source has a better fitness.
| دانلود رایگان مقاله انگلیسی + خرید ترجمه فارسی
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| عنوان فارسی مقاله: |
استقرار مبتنی بر P.S.O دودویی گسسته جدید شبکه های حسگر بی سیم |
| عنوان انگلیسی مقاله: |
Novel Discrete Binary P.S.O based deployment of Wireless Sensor Network |
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