Esheh Jehan, Affes Sofiene
EMT Centre (Energy, Materials and Telecommunications), INRS (Institut National de la Recherche Scientifique), Université du Québec, Montréal, QC H5A 1K6, Canada.
Sensors (Basel). 2024 Jan 10;24(2):0. doi: 10.3390/s24020430.
Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.
无线传感器网络（WSNs）因其灵活性和成本效益而广受欢迎，并广泛应用于各种传感器通信应用，特别是对于本地化是主要挑战的应用。此外，Dv-hop算法是WSNs中常用的一种无需测距的定位算法。尽管它简单且硬件要求低，但在定位精度方面确实存在局限性。在本文中，我们针对物联网（IoT）中的WSN应用开发了一种基于深度学习（DL）的精确无需测距定位方法。为了提高定位性能，我们利用深度神经网络（DNN）来校正未知节点（即位置未知）与锚节点（即位置已知）之间的估计距离，同时不会增加物联网的成本。深度学习需要大量训练数据才能产生准确的结果，深度神经网络也不例外。包括深度神经网络在内的机器学习的有效性取决于能否获取大量训练数据以实现最佳性能。然而，为了应对这一挑战，我们通过实施数据增强策略（DAS）提出了一种解决方案。该策略包括在现有的真实锚节点周围有策略地创建多个虚拟锚节点。因此，这个过程会生成更多的训练数据并显著增加数据量。我们证明DAS可以为深度神经网络提供足够的训练数据，并最终使WSNs和物联网更可行地充分受益于低成本的深度神经网络辅助定位。仿真结果表明，所提出的方法（带有DNN校正的Dv-hop）的精度超过了Dv-hop。
