Measurement Based Threat Aware Drone Base Station Deployment

Abstract

Unmanned aerial vehicles are gaining importance with many civilian and military applications. Especially the surveillance, search/rescue, and military operations may have to be carried out in extremely constrained environments. In such scenarios, drone base stations (DBSs) have to provide communication services to the people at the ground. The ground users may have no access to the global positioning system (GPS); therefore, their locations have to be estimated using alternative techniques. Besides there may be threats in the environment, such as shooters. In this work, we address the problem of optimal DBS deployment under the aforementioned constraints. We propose a novel DBS deployment algorithm that uses estimated positions of ground users and threats. The proposed algorithm is based on receiver signal strength-based maximum likelihood estimate of user locations and K-means clustering supported heuristic that takes into account the positions of threats. Numerical results show that proposed algorithm performs close to the computation intensive near-optimal algorithm and strikes a good trade-off between the number of unserved users and the probability of DBSs not being hit.