A localization algorithm for DV-Hop wireless sensor networks based on manhattan distance

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.