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Computational intelligence based localization of moving target nodes using single anchor node in wireless sensor networks

机译:基于无线传感器网络中的单锚点的移动目标节点的基于计算智能

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Wireless Sensor Networks (WSNs) have tremendous ability to interact and collect data from the physical world. The main challenges for WSNs regarding performance are data computation, prolong lifetime, routing, task scheduling, security, deployment and localization. In recent years, many Computational Intelligence (CI) based solutions for above mentioned challenges have been proposed to accomplish the desired level of performance in WSNs. Application of CI provides independent and robust solutions to ascertain accurate node position (2D/3D) with minimum hardware requirement (position finding device, i.e., GPS enabled device). The localization of static target nodes can be determined more accurately. However, in the case of moving target nodes, accurate position of each node in network is a challenging problem. In this paper, a novel concept of projecting virtual anchor nodes for localizing the moving target node is proposed using applications of Particle Swarm Intelligence, H-Best Particle Swarm Optimization, Biogeography Based Optimization and Firefly Algorithm separately. The proposed algorithms are implemented for range-based, distributed, non-collaborative and isotropic WSNs. Only single anchor node is used as a reference node to localize the moving target node in the network. Once a moving target node comes under the range of a anchor node, six virtual anchor nodes with same range are projected in a circle around the anchor node and two virtual anchor nodes (minimum three anchor nodes are required for 2D position) in surrounding (anchor and respective moving target node) are selected to find the 2D position. The performance based results on experimental mobile sensor network data demonstrate the effectiveness of the proposed algorithms by comparing the performance in terms of the number of nodes localized, localization accuracy and scalability. In proposed algorithms, problem of Line of Sight is minimized due to projection of virtual anchor nodes.
机译:无线传感器网络(WSNS)具有巨大的互动和收集物理世界的数据。 WSNS关于性能的主要挑战是数据计算,延长生命周期,路由,任务调度,安全性,部署和本地化。近年来,已经提出了许多基于基于计算的智能(CI)基于上述挑战的解决方案,以实现WSN中所需的性能水平。 CI的应用提供独立和强大的解决方案,以确定具有最小硬件要求的精确节点位置(2D / 3D)(位置查找设备,即支持GPS的设备)。可以更准确地确定静态目标节点的定位。然而,在移动目标节点的情况下,网络中每个节点的准确位置是一个具有挑战性的问题。本文使用粒子群智能,H-最佳粒子群优化,生物地理优化和萤火虫算法分别使用粒子群智能,H-最佳粒子群优化,生物地理优化和萤火虫算法,提出了一种用于定位移动目标节点的虚拟锚节点的新颖概念。所提出的算法用于基于范围的,分布式,非协同和各向同性WSN。只用单锚点用作参考节点以本地化网络中的移动目标节点。一旦移动的目标节点在锚节点的范围内,围绕锚点节点的圆形围绕六个虚拟锚点并且在周围的两个虚拟锚节点(最小三个锚节点)中突出了六个虚拟锚点(锚点选择各自的移动目标节点以找到2D位置。实验移动传感器网络数据的基于性能结果通过在本地化的节点数量,定位精度和可扩展性方面进行比较来证明所提出的算法的有效性。在所提出的算法中,由于虚拟锚点节点的投影,视线的问题被最小化。

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