Large Scale Distributed Localization Based on RSS and Mass-Spring Model

Large scale localization has gained interest in the last few years together with wireless sensor networks. Applications such as a swarm of drones or a sensor network to control an environment already exist but do not cover scales up to thousands of nodes. In this paper, a method for large scale distributed localization is developed based on existing technologies including received signal strength and mass-spring model (MSM). First, we created a theoretical model based on specific indoor and outdoor tests. Based on this model, a scaled up simulation was done using an MSM-based algorithm. Using this simulation, an average error of up to 7.66 m could be achieved in a field of 100 m by 100 m.