目标网络场景自适应的IP定位框架

Adaptive IP geolocation framework for target network scenarios

通过IP来定位目标位置是基于位置的服务的重要基础。当前研究者面向不同的网络场景,提出了不同实现原理的IP定位方法。但在不同的网络场景中,各类IP定位方法难以维持理想的定位效果。介绍了基于网络测量的IP定位的3种典型实现原理,分析了各类方法在不同网络场景下的优缺点,并提出了一种目标网络场景自适应的IP定位框架。该定位框架将目标在地标库中进行位置对比,初步判定目标的城市级位置并分布式部署探测源;随后获取目标所在城市的时延、拓扑、同子网地标等属性,并判定其所处网络场景;根据目标网络场景,选择合适类型的定位算法对目标IP进行定位,输出位置估计结果。通过在我国11个城市进行模拟定位实验,得到了在不同数量和分布的地标数据支撑下各类IP定位方法的多层次表现,结果显示所提框架的城市级定位成功率可达96.16%,街道级定位中值误差可达4.13 km,同时所提框架在不同的同子网地标数量和目标可达性条件下均能保持稳定的定位效果。实验结果验证了所提框架的有效性,并为IP定位研究提供新的思路。

The target location can be determined through IP geolocation,serving as a vital foundation for location-based services.Researchers have proposed various IP geolocation algorithms with different implementation principles to cater to different network scenarios.However,maintaining an ideal geolocation effect proves challenging for these algorithms in diverse network scenarios.Three typical implementation principles for IP geolocation based on network measurement were introduced.The advantages and disadvantages of these methods in various network scenarios were analyzed,and an adaptive IP geolocation framework specifically tailored to target network scenarios was proposed.The geolocation framework was functioned as follows:initially,a preliminary city-level location estimation was obtained by comparing the target’s location with the landmark database.Then,detection sources were deployed in a distributed manner,and information such as delay,topology and same subnet landmarks for the target city was gathered to determine the network scenario.Finally,an appropriate geolocation method was employed to accurately estimate the target’s location according to the identified network scenario.Through simulation geolocation experiments in 11 cities of China,the multi-level performance of various IP geolocation methods supported by landmark data of different quantities and distributions was evaluated.The results indicate that the proposed framework achieves a city-level geolocation success rate of 96.16%and a median error of 4.13 km for street-level geolocation.Moreover,the framework demonstrates stable geolocation performance across different conditions,including varying same subnet landmark numbers and target accessibility.These experimental findings validate the effectiveness of the proposed framework and offer novel insights for IP geolocation research.