فایل ورد کامل استقرار حسگر مبتنی بر PSO دودویی گسسته اصلاح شده جهت همگرایی در شبکه های WSN

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تعداد صفحات این فایل: ۱۶ صفحه

در این مقاله، مسئله استقرار حسگر برای مکان یابی اهداف تحت محدودیت ( پوشش کامل شبکه های حسگر) در نظر گرفته شده است. ابتدا راجع به مسئله پوشش شبکه حسگر توضیح داده شده و سپس از الگوریتم PSO دودویی اصلاح شده برای حل مسئله استفاده شده است. نتیجه بدست آمده نشان داد که الگوریتم MDPSO توانایی تشخیص موثرتر راه حل بهینه سازی در مدت زمان محدود را دارد، بدین طریق استقرار حسگرها، پوشش فیلد حسگر را افزایش می دهد. به علاوه، الگوریتم پیشنهادی مفید، مقیاس پذیر و بادوام تر می باشد. 

عنوان انگلیسی:Modified Discrete Binary PSO based Sensor Placement for Coverage in WSN Networks~~en~~

INTRODUCTION A wireless sensor network (WSN) is one of the communication networks that have been used nowadays. A typical wireless sensor network consists of thousands of sensor nodes, deployed either randomly or according to some predefined statistical distribution, over a geographic region of interest. Spatially distributed sensors are employed in WSN to monitor environmental or physical conditions, vibration, pressure, motion, temperature and pollutant. The range of potential applications that WSNs are envisaged to support, is tremendous .Some WSN application area military applications like communication systems, commanding, reconnaissance patrols, looking –out etc, environmental and natural resource monitoring , medical, industrial, robot, air forecasting, security, anti terrorism applications and civilian applications. Cost and size constraints on sensor nodes yield subsequent constraints on resources such as bandwidth, energy, computational speed and memory .These limitations have given many technical problems such as routing, scheduling and coverage and cost. Distributed sensor network can arrange in two ways, one as a random placement and the second as a grid-based placement. When the surrounding is unknown the random placement is used but when the properties of the network were known before then the sensor placement could be done with great investigation so that we could guarantee the quality of services. The strategy of sensor placement depends on the application of the distributed sensor network (DNS). In this paper we focus on the gird-based placement. The sensor network which is based on grid-based plcement is considered as a two or three dimensional network. And we applied the modified binary PSO algorithm for solve the problems like coverage and cost. II. PSO: A BRIEF OVERVIEW PSO is a population-based optimization algorithm, inspired by the social behaviour of flocks of birds or fishes. Each particle is an individual and the swarm is composed of particles. The problem solution space is formulated as a search space. Each position in the search space is a correlated solution of the problem. In a PSO system, each particle is “flown” through the multidimensional search space, adjusting its position in search space according to its own experience and that of neighboring particles. Suppose a group of birds are searching for food in a place randomly and food is available in one part of searching area and the birds have no information about the place where the food is available and they only know their distance to the food source. The adopted strategy by birds is that they follow the bird which has minimum distance to the food source. 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. There are many subjects which have discrete nature and because there are many problems which have a discrete nature and also because many of both discrete and continuous problems can be solved in a discrete space so there is a need to use the binary PSO algorithm. The features of the method are as follows: (1) The method is based on researches on swarms such as fish schooling and bird flocking. (2) It is based on a simple concept. Therefore, the computation time is short and it requires few memories [4]. A. The PSO Algorithm PSO is developed through simulation of bird flocking in two-dimension space. The position of each individual particle is represented by XY axis position and also the velocity is expressed by VX (the velocity of X axis) and Vy (the velocity of Y axis). Modification of the particle position is realized by the velocity and position information. Each particle knows its best value pbest and its XY position. Moreover, each particle knows the best value in the group gbest among pbest. Each particle tries to change its position using the following information [4]: a) The current velocities (Vx, Vy), b) The distance between the current position, and pbest and gbest. c) The current positions (x, y)

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