A System for Network Asset Discovery and Localization Using Edge Computing Drones
Zhe Zhang, Yingjie Wang, Guoliang Hu, Hongjie Fan, Songtao Ye · 2025
As network technologies rapidly advance, complexity in network asset management have become increasingly evident. Traditional methods protect network assets by segregating external and internal networks. However, this approach increases management complexity and brings potential security risks. Additionally, existing network mapping tools often lack the ability to accurately locate network assets. This limitation prevents administrators from easily identifying the geographic location of network assets. As a result, effective network asset management is hindered. To address these challenges, this study introduces an automated network mapping solution. The solution combines drone with edge computing devices. Drones with edge computing devices use active routers as access points for nighttime mapping. This approach overcomes the limitations of traditional methods and provides a novel idea for managing network assets.