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首页> 外文期刊>Applied Soft Computing >A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization
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A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization

机译：一种混合模型，采用模糊逻辑和带矢量粒子群优化无线传感器网络定位的极端学习机

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摘要

Localization is one of the challenges in wireless sensor networks, especially those without the aid of a global positioning system. Use of a dedicated positioning device incurs additional cost and reduces battery life; therefore, a range-free localization scheme is promising as a cost-effective approach. However, the main limitation of this approach is that the estimation precision can be affected by factors such as node density, sensing coverage, and topology diversity. Thus, this study investigates and proposes a method for improving a traditional range-free-based localization method (centroid) that uses soft computing approaches in a hybrid model. This model integrates a fuzzy logic system into centroid and uses an extreme learning machine (ELM) optimization technique to capitalize on the strengths of both approaches: the former is properly used with low node density and short coverage, while the latter is used for the opposite-to achieve a robust location estimation scheme. The ratios of known nodes within the sensing coverage range to the total known nodes and of the sensing coverage range to the maximum coverage range are used as adaptive weights for this hybrid model. To further improve the efficiency, especially in heterogeneous topologies, the concept of resultant force vectors is applied to this hybrid model over particle swarm optimization to mitigate the effects of irregular deployments. The performance of the proposed method is extensively evaluated via simulations that demonstrate its effectiveness compared to other state-of-the-art soft-computing-based range-free localization schemes (i.e., centroid, a fuzzy logic system, and a support vector machine with a traditional ELM). (c) 2018 Elsevier B.V. All rights reserved.
机译：本地化是无线传感器网络中的挑战之一，尤其是那些没有全球定位系统的挑战。使用专用定位设备会引发额外的成本并降低电池寿命;因此，无距离定位方案是具有成本效益的方法。然而，这种方法的主要限制是估计精度可能受到节点密度，感测覆盖和拓扑多样性的因素的影响。因此，本研究调查并提出了一种改进一种改进传统无距离的定位方法（质心）的方法，该方法在混合模型中使用软计算方法。该模型将模糊逻辑系统集成到质心中，并使用极端学习机（ELM）优化技术来利用两种方法的优势：前者与低节点密度和短覆盖率合理地使用，而后者用于相反的情况 - 达到鲁棒位置估计方案。将传感覆盖范围内的已知节点的比率与总已知节点的总和和最大覆盖范围的传感范围内的差值用作该混合模型的自适应权重。为了进一步提高效率，特别是在异构拓扑中，将所得力向量的概念应用于该混合模型，通过粒子群优化来减轻不规则部署的影响。通过模拟广泛地评估所提出的方法的性能，这些模拟与其他基于现有的基于软计算的无级定位方案（即质心，模糊逻辑系统和支持向量机器相比，其有效性与传统的榆树）。 （c）2018 Elsevier B.v.保留所有权利。

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