Group Behavior Impact on an Opportunistic Localization Scheme
In this paper, the authors tackled the localization problem from an opportunistic perspective, according to which a node can infer its own spatial position by exchanging data with passing by nodes, called peers. They consider an opportunistic localization algorithm based on the Linear Matrix Inequality (LMI) method coupled with a weighted barycenter algorithm. This scheme has been previously analyzed in scenarios with random deployment of peers, proving its effectiveness. In this paper, the authors extend the analysis by considering more realistic mobility models for peer nodes. More specifically, they consider two mobility models, namely the Group Random Waypoint Mobility Model and the Group Random Pedestrian Mobility Model, which is an improvement of the first one.