Localization Wireless Sensor Networks (WSN) represent a research topic with increasing interest due to their numerous applications. However, the viability of these systems is compromised by the attained localization uncertainties once implemented, since the network performance is highly dependent on the sensors location. The Node Location Problem (NLP) aims to obtain the optimal distribution of sensors for a particular environment, a problem already categorized as NP-Hard. Furthermore, localization WSN usually perform a sensor selection for determining which nodes are to be utilized for maximizing the achieved accuracy. This problem, defined as the Sensor Selection Problem (SSP), has also been categorized as NP-Hard. While different metaheuristics have been proposed for attaining a near optimal solution in both problems, no approach has considered the two problems simultaneously, thus resulting in suboptimal solutions since the SSP is biased by the actual node distribution once deployed. In this paper, a combined approach of both problems simultaneously is proposed, thus considering the SSP within the NLP. Furthermore, a novel metaheuristic combining the Black Widow Optimization (BWO) algorithm and the Variable Neighbourhood Descent Chains (VND-Chains) local search, denominated as BWO-VND-Chains, is particularly devised for the first time in the author’s best knowledge for the NLP, resulting in a more efficient and robust optimization technique. Finally, a comparison of different metaheuristic algorithms is proposed over an actual urban scenario, considering different sensor selection criteria in order to attain the best methodology and selection technique. Results show that the newly devised algorithm with the SSP criteria optimization achieves mean localization uncertainties up to 19.66 % lower than traditional methodologies.

中文翻译：

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减少定位无线传感器网络中目标不确定性的黑寡妇优化

定位无线传感器网络（WSN）因其众多的应用而成为人们越来越感兴趣的研究主题。然而，这些系统的可行性会受到实施后所获得的定位不确定性的影响，因为网络性能高度依赖于传感器的位置。节点位置问题 (NLP) 旨在获得特定环境下传感器的最佳分布，该问题已被归类为 NP-Hard 问题。此外，定位 WSN 通常会执行传感器选择，以确定要利用哪些节点来最大化所达到的精度。这个问题被定义为传感器选择问题（SSP），也被归类为 NP 难问题。虽然已经提出了不同的元启发法来在这两个问题中获得接近最优的解决方案，但没有一种方法可以同时考虑这两个问题，从而导致次优解决方案，因为 SSP 一旦部署就会​​受到实际节点分布的偏差。本文提出了同时解决这两个问题的组合方法，从而在 NLP 中考虑了 SSP。此外，结合了黑寡妇优化（BWO）算法和可变邻域下降链（VND-Chains）局部搜索的新颖元启发式算法，称为 BWO-VND-Chains，是作者根据作者的最佳知识首次专门设计的。 NLP，带来更高效、更稳健的优化技术。最后，在实际城市场景中比较不同的元启发式算法，考虑不同的传感器选择标准，以获得最佳的方法和选择技术。结果表明，采用 SSP 标准优化的新设计算法的平均定位不确定性比传统方法降低了 19.66%。