WSNs (Wireless Sensor Networks) are critical components of the Internet of Things (IoT). With the internationalization of the IoT and the widespread use of apps, it is crucial to increase WSNs localization algorithms' accuracy and their flexibility to dynamic and changing surroundings. To this end, it is proposed in this article a wireless sensor network location algorithm based on Manhattan distance (MDV-Hop) to solve many existing problems encountered in the wireless sensor network location algorithm. The MDV-Hop localization algorithm improves over present algorithms regarding frequency hopping, length, and least-squares of nodes to enhance the WSNs nodes' location accuracy and algorithm's adaptability in multi-variable environments. Manhattan distance has the characteristics of the sum of the projection distance of the line segment between two points on the coordinate axis in Euclidean space. The Manhattan distance measurement method is combined with Euclidean distance to determine the second minimum frequency hopping between beacon nodes, which substantially increases the DV-Hop algorithm's localization performance. On this foundation, the multi-objective genetics (NSGA-II) algorithm is employed to refine the outcomes of the least-squares approach to increase the suggested algorithm's localization accuracy, as it succeeds the simplicity and flexibility of the original DV-Hop method. Extensive simulations are performed in network scenarios with sparse and unevenly distributed anisotropic sensor nodes, and experimental results show that the MDV-Hop method outperforms the current WSNs node localization techniques in terms of performance and precision.

WSN（无线传感器网络）是物联网 (IoT) 的关键组件。随着物联网的国际化和应用程序的广泛使用，提高 WSN 定位算法的准确性及其对动态和变化环境的灵活性至关重要。为此，本文提出了一种基于曼哈顿距离的无线传感器网络定位算法（MDV-Hop），以解决无线传感器网络定位算法中存在的诸多问题。MDV-Hop定位算法在节点跳频、长度、最小二乘等方面进行了改进，提高了WSNs节点的定位精度和算法在多变量环境中的适应性。曼哈顿距离具有欧几里得空间中坐标轴上两点之间的线段投影距离之和的特点。曼哈顿距离测量方法结合欧几里得距离确定信标节点之间的第二最小跳频，大大提高了DV-Hop算法的定位性能。在此基础上，采用多目标遗传学（NSGA-II）算法改进最小二乘法的结果，以提高建议算法的定位精度，因为它继承了原始 DV-Hop 方法的简单性和灵活性。在具有稀疏和不均匀分布的各向异性传感器节点的网络场景中进行了广泛的模拟，