Location information of a sensor node is the primary concern to process the sensed data in Wireless Sensor Networks (WSNs). The location of the sensor node is used in other domains of sensor network like message routing, node tracking, load balancing. For statically deployed sensor nodes, mobile anchor based localization is an efficient solution. The main challenge in mobile anchor based localization is designing an optimum path for the mobile anchor node considering the coverage, path length and localizability of sensor nodes as the key features. In this paper, we propose a novel path planning approach for mobile anchor based localization called “M-Curves”. Our proposed model promises that all the nodes in the network will receive at least three non-collinear beacon messages for localization. Our proposed trajectory assures full coverage, high localization accuracy as compared to other static models. Also, we optimize the localization process by using Dolphin Swarm Algorithm(DSA). The fitness function used for optimization in DSA, minimizes the localization error of the node in the network.

传感器节点的位置信息是在无线传感器网络（WSN）中处理感测数据的主要问题。传感器节点的位置在传感器网络的其他域中使用，例如消息路由，节点跟踪，负载平衡。对于静态部署的传感器节点，基于移动锚的定位是一种有效的解决方案。基于移动锚的定位中的主要挑战是，将传感器节点的覆盖范围，路径长度和可定位性作为关键特征，为移动锚节点设计最佳路径。在本文中，我们为基于移动锚的本地化提出了一种新颖的路径规划方法，称为“ M曲线”。我们提出的模型保证网络中的所有节点将至少接收三个非共线信标消息以进行定位。我们建议的轨迹可确保全面覆盖，与其他静态模型相比，定位精度更高。此外，我们通过使用海豚群算法（DSA）优化了定位过程。DSA中用于优化的适应性函数可最大程度地减少网络中节点的定位错误。