Underwater sensor networks (USNs) are envisioned to enable a large variety of marine applications. Such applications require accurate position information of sensor nodes. However, the openness and inhomogeneity characteristics of underwater medium make it much more challenging to solve the localization issue. This paper is concerned with a privacy-preserving localization issue for USNs in inhomogeneous underwater medium. An honest-but-curious model is considered to develop a privacy-preserving localization protocol. Based on this, a localization problem is constructed for sensor nodes to minimize the sum of all measurement errors, where a ray compensation strategy is incorporated to remove the localization bias from assuming the straight-line transmission. To make the above problem tractable, we consider the unsupervised, supervised and semisupervised scenarios, through which deep reinforcement learning (DRL) based localization estimators are utilized to estimate the positions of sensor nodes. It is noted that, the proposed localization solution in this paper can hide the private position information of USNs, and more importantly, it is robust to local optimum for nonconvex and nonsmooth localization problem in inhomogeneous underwater medium. Finally, simulation studies are given to show the position privacy can be preserved, while the localization accuracy can be enhanced as compared with the other existing works.

中文翻译：

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通过深度强化学习对水下传感器网络进行隐私保护的本地化

可以设想使用水下传感器网络（USN）来实现多种海洋应用。这样的应用需要传感器节点的准确位置信息。然而，水下介质的开放性和非均质性使得解决定位问题更具挑战性。本文涉及非均匀水下介质中USN的隐私保护本地化问题。考虑建立一个诚实但好奇的模型来开发一个保护隐私的本地化协议。基于此，为传感器节点构造了一个定位问题，以使所有测量误差的总和最小化，其中并入了光线补偿策略，以消除假设直线传输时的定位偏差。为了使上述问题更容易处理，我们认为是无监督的，监督和半监督方案，通过这些方案，可以使用基于深度增强学习（DRL）的定位估计器来估计传感器节点的位置。值得注意的是，本文提出的定位解决方案可以隐藏USN的私人位置信息，更重要的是，对于非均匀水下介质中的非凸和非光滑定位问题，它对于局部最优具有鲁棒性。最后，通过仿真研究表明，与其他现有工作相比，可以保留位置隐私，同时可以提高定位精度。本文提出的定位解决方案可以隐藏USN的私人位置信息，更重要的是，对于非均匀水下介质中的非凸和非光滑定位问题，它对于局部最优具有鲁棒性。最后，通过仿真研究表明，与其他现有工作相比，可以保留位置隐私，同时可以提高定位精度。本文提出的定位解决方案可以隐藏USN的私人位置信息，更重要的是，对于非均匀水下介质中的非凸和非光滑定位问题，它对于局部最优具有鲁棒性。最后，通过仿真研究表明，与其他现有工作相比，可以保留位置隐私，同时可以提高定位精度。