دانلود ترجمه مقاله مکان یابی گره توزیع شده با الگوریتم بهینه سازی توده ذرات باینری (BPSO)

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چکیده
مقدمه
کارهای وابسته
الگوریتم پیشنهادی
نتایج شبیه سازی و بحث
نتیجه گیری

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در این مقاله یک الگوریتم بهینه سازی ازدحام ذرات دودویی برای مکان یابی گره توزیع شده در شبکه های حسگر بی سیم پیشنهاد کردیم. هر گره ناشناخته تحت شرایط اندازه گیری فاصله از سه یا چند گره اتصال همسایه، مکان یابی را اجرا می کند. سپس از گره های مستقر شده به عنوان مرجع برای گره های باقیمانده استفاده می شود. فاصله از گره اتصال تا گره ناشناخته با استفاده از نشانگر قدرت سیگنالهای دریافتی محاسبه می گردد. به عنوان معیار عملکرد، از خطای مکان یابی و زمان محاسبه در طول شبیه سازیها در Matlab استفاده می گردد. نتایج شبیه سازی نشان می دهد PSO مختصات گره را درست تر تعیین می کند، اما BPSO این کار را سریعتر انجام می دهد.

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Introduction Localization is a process to find out the locations of sensor nodes which are deployed randomly in the sensing area [1]. Generally, localization processes equip each node with the global positioning system (GPS), which is not effective due to size, cost and power consumption constraints. Therefore, many localization algorithms in wireless sensor network (WSN) use special nodes called anchors to estimate the location of unknown nodes by using a priori knowledge of the anchors nodes’ coordinates. In other words, they need anchors being aware of their position and the unknown nodes measure the distances or angles from anchors nodes to estimate their positions [2]. Related Work PSO is a population-based stochastic optimization technique developed by Kennedy and Eberhart in 1995 [3]. A summary of applications of PSO in WSN can be found in [4]. In [5], the authors proposed a localization method for WSN with unknown nodes based on a priori information of locations of anchors using PSO algorithm. The proposed scheme used PSO to minimize the localization error. This approach is a centralized architecture for the WSN, which requires a lot of communication that may lead to congestions, delays, and also energy exhaustion. In contrast, Kulkarni et al. proposed distributed iterative node localization in WSN using PSO algorithm [6]. An unknown node which has three or more anchors in its communication range runs PSO to minimize the localization error which used same function as [5]. This method does not require that each node transmit its range measurement to a central node. In addition, it can localize all nodes that have three localized nodes or anchors in their communication range. However, the localization based on PSO requires complex computations that cause relatively large computation power and longer computation time. Proposed Algorithm Binary Particle Swarm Optimization (BPSO). Unlike PSO, the BPSO algorithm was used in binary discrete search spaces. Kennedy and Eberhart have implemented the BPSO to search in binary discrete search spaces, by applying a sigmoid transformation to the velocity component to force the velocities into the value between 0 and 1, and the position of the particles to be either 0 or 1. Basically, PSO and BPSO are the same in the sense of the logical flow of the algorithm, which in finding pbest and gbest, update velocity and position [7]. The only difference between PSO and BPSO is the equations used to define the updates of velocity and position of each particle. Equation (1) is used to update the velocity while (2) and (3) are used to update the position of each particle.