Event localization plays a fundamental role in many wireless-sensor network applications, such as environmental monitoring, homeland security, medical treatment, and health care, and it is essentially a nonconvex and nonsmooth problem. In this paper, we address such a problem in a completely decentralized way based on augmented Lagrangian methods and alternating direction method of multipliers (ADMM). A decentralized algorithm is proposed to solve the nonsmooth and nonconvex event localization problem directly, rather than using conventional convex relaxation techniques. The avoidance of convex relaxation is significant in that convex relaxation-based methods generally suffer from high computational complexity. The convergence properties are also evaluated and substantiated using numerical simulations, which show that the proposed algorithm achieves better localization accuracy than existing projection-based approaches when the target is within the convex hull of localization sensors. When the target is outside the convex hull, numerical simulations show that the proposed approach has a higher probability to converge to the target event location than existing projection-based approaches. Numerical simulation results show that our approach is also robust to network topology changes.

中文翻译：

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通过非凸非光滑ADMM和增强拉格朗日方法进行传感器网络事件定位

事件定位在许多无线传感器网络应用（例如环境监控，国土安全，医疗和卫生保健）中扮演着基本角色，并且本质上是一个非凸且不平滑的问题。在本文中，我们基于增强拉格朗日方法和乘数交替方向方法（ADMM），以完全分散的方式解决此类问题。提出了一种分散算法来直接解决非光滑和非凸事件的定位问题，而不是使用传统的凸松弛技术。凸松弛的避免是很重要的，因为基于凸松弛的方法通常会遭受较高的计算复杂度。还使用数值模拟对收敛性进行了评估和证实，结果表明，当目标位于定位传感器的凸包内时，与现有的基于投影的方法相比，该算法具有更好的定位精度。当目标位于凸包之外时，数值模拟表明，与现有的基于投影的方法相比，所提出的方法具有更高的收敛到目标事件位置的可能性。数值仿真结果表明，我们的方法对于网络拓扑变化也很健壮。