On the Path to High Precise IP Geolocation: A Self-Optimizing Model
This work addresses the problem of accurate IP geolocation for applications like Content Delivery Networks, representing an incremental improvement over existing methods.
The paper tackles the challenge of achieving precise and stable IP geolocation by introducing a self-optimizing model that uses optimized landmark positions and network distance approximations based on road networks, evaluated under real-world conditions in Europe with results showing improved accuracy.
IP Geolocation is a key enabler for the Future Internet to provide geographical location information for application services. For example, this data is used by Content Delivery Networks to assign users to mirror servers, which are close by, hence providing enhanced traffic management. It is still a challenging task to obtain precise and stable location information, whereas proper results are only achieved by the use of active latency measurements. This paper presents an advanced approach for an accurate and self-optimizing model for location determination, including identification of optimized Landmark positions, which are used for probing. Moreover, the selection of correlated data and the estimated target location requires a sophisticated strategy to identify the correct position. We present an improved approximation of network distances of usually unknown TIER infrastructures using the road network. Our concept is evaluated under real-world conditions focusing Europe.