Wireless sensor networks have been considered as an emerging technology for numerous applications of cyber-physical systems. These applications often require the deployment of sensor nodes in various anisotropic fields. Localization in anisotropic fields is a challenge because of the factors such as non-line of sight communications, irregularities of terrains, and network holes. Traditional localization techniques, when applied to anisotropic or irregular fields, result in colossal location estimation errors. To improve location estimations, this paper presents a comparative analysis of available localization techniques based on taxonomy framework. A detailed discussion on the importance of localization of sensor nodes in irregular fields from the reported real-life applications is presented along with challenges faced by existing localization techniques. Further, taxonomy based on techniques adopted by localization methods to address the effects of irregular fields on location estimations is reported. Finally, using the designed taxonomy framework, a comparative analysis of different localization techniques addressing irregularities and the directions towards the development of an optimal localization technique is addressed.

无线传感器网络已经被认为是网络物理系统众多应用中的新兴技术。这些应用通常需要在各个各向异性领域中部署传感器节点。由于视线通讯不畅，地形不规则和网孔等因素，各向异性场的定位是一个挑战。当将传统的定位技术应用于各向异性或不规则场时，会导致巨大的位置估计误差。为了改进位置估计，本文对基于分类框架的可用定位技术进行了比较分析。提出了有关已报告的现实应用中不规则字段中传感器节点定位的重要性的详细讨论，以及现有定位技术面临的挑战。此外，报告了基于定位方法以解决不规则字段对位置估计的影响的技术的分类法。最后，使用设计的分类学框架，针对解决不规则性的不同本地化技术进行了比较分析，并提出了开发最佳本地化技术的方向。