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Range-free localization using Reliable Anchor Pair Selection and Quantum-behaved Salp Swarm Algorithm for anisotropic Wireless Sensor Networks
Ad Hoc Networks ( IF 4.8 ) Pub Date : 2020-12-29 , DOI: 10.1016/j.adhoc.2020.102406
Qiang Tu , Yitong Liu , Feng Han , Xingcheng Liu , Yi Xie

Localization is one of the essential problems in the Internet of Things (IoT) and other wireless sensor applications. Most traditional range-free localization algorithms ignore the anisotropy factors, which are frequently observed in Wireless Sensor Networks (WSNs) and result in low positioning precision. To mitigate the impact of anisotropy on localization, we propose an accurate localization approach based on Reliable Anchor Pair Selection (RAPS) and Quantum-behaved Salp Swarm Algorithm (QSSA) for anisotropic networks. First, the proposed algorithm uses hop count threshold to limit the number of message transmissions between nodes, which assists to decrease communication overhead. Next, based on the geometric constraints, the selected reliable anchor pairs are divided into two types, namely, super anchor pairs and suboptimal ones. Then, we design different distance estimation equations for the reliable anchor pairs to reduce ranging error. Finally, the QSSA is introduced to calculate the coordinates of regular nodes, which tends to lower the impact of anisotropy factors and improve location accuracy. Extensive simulations show that the proposed algorithm outperforms state-of-the-art algorithms in terms of accuracy and robustness against network anisotropy.



中文翻译:

各向异性无线传感器网络使用可靠锚对选择和量子行为Salp Swarm算法进行无范围定位

本地化是物联网(IoT)和其他无线传感器应用程序中的基本问题之一。大多数传统的无范围定位算法都忽略了各向异性因素,这些因素在无线传感器网络(WSN)中经常出现,并导致定位精度低。为了减轻各向异性对定位的影响,我们针对各向异性网络提出了一种基于可靠锚对选择(RAPS)和量子行为的Salp Swarm算法(QSSA)的精确定位方法。首先,所提出的算法使用跳数阈值来限制节点之间消息传输的数量,这有助于减少通信开销。接下来,根据几何约束,将所选择的可靠锚对分为超级锚对和次优锚对两种。然后,我们为可靠的锚对设计了不同的距离估计方程,以减少测距误差。最后,引入QSSA来计算规则节点的坐标,这往往会降低各向异性因素的影响并提高定位精度。大量的仿真表明,在针对网络各向异性的准确性和鲁棒性方面,该算法优于最新算法。

更新日期:2021-01-04
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