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Sensor data with geographical information become ubiquitous with development of location base applications in various businesses. There is no exception to Wireless sensor network (WSN) as it is getting more practical under the booming of semiconductor industry. Localization of WSN usually depends on beacons that equip with GPS or GSM module. The cost of finance and energy hamper the application of WSN. Robot or flight vehicle called Mobile Beacon (MB) can relieve the issue and enhance the application area of WSN. Based on the framework we proposed in , we renovate the algorithm to find the best position for MB in every step by grouping weight of cosine similarity to mining the relation between responsible sensors. The MB machine works in an autonomous mode to find dynamic path without any supervision. In simulation, it is proved that the novel method makes the best of observation and covers unknown sensors as many as possible without heavy computation and struggle of energy. With appropriate parameters, the method can reach a 90% average coverage rate on any random distribution of sensors.