Optimum deployment of sensors in WSNs

Samayveer Singh, S. Chand, B. Kumar
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引用次数: 6

Abstract

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.
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传感器在无线传感器网络中的优化部署
蚁群算法是求解优化问题的重要技术之一。它已被用于在网格环境[12]中寻找部署传感器的位置,其中目标称为兴趣点(PoI),位于正方形网格中的网格点上。传感器的位置是网格点,通过考虑汇聚位置作为部署传感器的起点来确定。虽然该工作提供了相对于给定的接收器位置覆盖所有目标的最佳传感器数量,但它没有提供哪个接收器位置提供覆盖目标的最小传感器数量。在本文中,我们使用蚁群算法在网格的所有可用位置中找到传感器数量最少的汇聚位置。在我们的算法中,我们计算在传感器范围内的目标与传感器的距离之和。然后我们把网格中所有传感器的总和加起来。这个距离对应于给定的接收器位置。我们通过改变网格中的汇聚位置来重复计算距离的相同过程。我们选择距离最小的接收器位置,这个接收器位置需要最少的传感器数量来覆盖所有目标。我们进行了仿真,以证明我们提出的工作的有效性。
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