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Optimum deployment of sensors in WSNs

机译:WSN中传感器的最佳部署

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Ant Colony Optimization (ACO) is one of the important techniques for solving optimization problems. It has been used to find locations to deploy sensors in a grid environment [12], in which the targets, called point of interest (PoI), are located on grid points in a square grid. The locations of sensors, which are grid points, are determined by considering the sink location as the starting point for deploying sensors. Though that work provides optimum number of sensors to cover all targets with respect to the given sink location, yet it does not provide which sink location provides minimum number of sensors to cover the targets. In this paper, we use ACO technique and find the sink location for which the number of sensors is minimum among all available locations in the grid. In our algorithm, we compute sum of distances of the targets from that sensor, which are in its range. Then we add these sums for all sensors in the grid. This distance corresponds to the given sink location. We repeat same process for computing the distance by changing the sink location in the grid. We choose that sink location for which the distance is minimum and this sink location requires minimum number of sensors to cover all targets. We carry out simulations to demonstrate the effectiveness of our proposed work.
机译:蚁群优化(ACO)是解决优化问题的重要技术之一。它已被用来寻找在网格环境中部署传感器的位置[12],其中目标(称为兴趣点(PoI))位于正方形网格的网格点上。通过将水槽位置作为部署传感器的起点来确定传感器的位置(它们是网格点)。尽管这项工作提供了最佳数量的传感器来覆盖相对于给定水槽位置的所有目标,但是它没有提供哪个水槽位置提供了最少数量的传感器来覆盖目标。在本文中,我们使用ACO技术并找到网格中所有可用位置中传感器数量最少的接收器位置。在我们的算法中,我们计算目标与该传感器的距离之和,该距离在其范围内。然后,我们将这些总和添加到网格中的所有传感器。该距离对应于给定的水槽位置。我们通过更改网格中的接收器位置重复相同的过程来计算距离。我们选择距离最小的接收器位置,并且该接收器位置需要最少数量的传感器才能覆盖所有目标。我们进行模拟以证明我们提出的工作的有效性。

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