Towards distributed geolocation by employing a delay-based optimization scheme

To support position-dependent services, like matchmaking algorithms for online games or geographic backup routes, the estimation of peer locations became a key requisite for a range of applications, recently. However, exact localization may be impossible, e.g., due to nodes lacking Global Positioning System (GPS) access for reasons of cost, energy, or signal unavailability. Alternative approaches, e.g., by nearby WLAN BSSIDs or IP geolocation, rely on databases and normally contain large outliers, in particular when concerning underrepresented mapping locations. This led us to the study of a complementary idea: By embedding nodes on a sphere and periodically minimizing local positioning errors by delay-based multilateration, we efficiently estimate node positions by distributed means, given a fair amount of position hints. Based on simulations that rely on real-world PlanetLab latency data, we show that global-scope peer locations can be estimated with an accuracy of a few hundred kilometers, where the novel approach outperforms a previously proposed spring-mass-based method by about 50%.