A System for Network Asset Discovery and Localization Using Edge Computing Drones

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.

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Publication Details

Published
2025-04-11
DOI
https://doi.org/10.1109/wccct65447.2025.11027997
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

A System for Network Asset Discovery and Localization Using Edge Computing Drones

Hongjie Fan, Guoliang Hu, Yingjie Wang, Zhe Zhang et al.
Network Security and Intrusion Detection
article

A System for Network Asset Discovery and Localization Using Edge Computing Drones

Hongjie Fan, Guoliang Hu, Yingjie Wang, Zhe Zhang, Songtao Ye
article en

Abstract

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.

China University of Political Science and Law (CN), National Computer Network Emergency Response Technical Team/Coordination Center of Chinar (CN), Xiangtan University (CN)
China University of Political Science and Law
Openalex Percentile: Top 86%
Network Security and Intrusion Detection
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