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首页> 外文期刊>International Journal of Distributed Sensor Networks >Hybrid Fuzzy Centroid with MDV-Hop BAT Localization Algorithms in Wireless Sensor Networks
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Hybrid Fuzzy Centroid with MDV-Hop BAT Localization Algorithms in Wireless Sensor Networks

机译:无线传感器网络中具有MDV-Hop BAT定位算法的混合模糊质心

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Many applications employing wireless sensor networks have been available in real-world scenarios. Their popularity is due to distinctive characteristics, for example, small scale, multisensing capability, and cost-effective deployment. However, there are constraints including routing, reliability, and especially localization, in particular without the aid of global positioning services, the lack of satellite coverage. In addition, if embedded, the overhead will be increased with hardware costs and shortened battery life. Thus, a range-free-based localization scheme is promising and is being pursued as a cost-effective approach. Centroid is one of the pioneer low complexity range-free estimation algorithms, and DV-Hop is another algorithm that has no requirements for distance information. However, their main drawbacks are location estimation precision. Recently, a soft-computing-based approach used to address uncertainty and approximation has been proposed as a low cost solution to gain precision, and, therefore, this research investigates its integration and then proposes a novel hybrid localization algorithm utilizing key characteristics of Centroid and DV-Hop. This hybrid scheme employs an extra weight with signal normalization derived from a fuzzy logic function in Centroid. The research also integrates a BAT algorithm of the modified DV-Hop. These combinations demonstrate the effectiveness in the simulation and location error reduction with time complexity trade-off.
机译:在现实世界中,已经有许多采用无线传感器网络的应用。它们之所以受欢迎,是因为其具有鲜明的特征,例如小规模,多感测能力和经济高效的部署。但是,存在一些限制,包括路由,可靠性,尤其是本地化,特别是在没有全球定位服务的帮助下,卫星覆盖不足。另外,如果是嵌入式的,则开销将随硬件成本而增加并缩短电池寿命。因此,基于无范围的定位方案是有前途的,并且正在作为一种具有成本效益的方法而被追求。质心是先锋的低复杂度无范围估计算法之一,而DV-Hop是另一种不需要距离信息的算法。但是,它们的主要缺点是位置估计精度。近来,已提出一种基于软计算的方法来解决不确定性和逼近问题,以此作为获得精度的低成本解决方案,因此,本研究对其进行了研究,然后提出了一种利用质心和特征的关键特性的新型混合定位算法。 DV跳。这种混合方案采用了额外的权重,并从质心中的模糊逻辑函数得出信号归一化。该研究还集成了改进的DV-Hop的BAT算法。这些组合证明了在仿真和减少位置误差以及时间复杂度折衷方面的有效性。

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