Measurement based threat aware drone base station deployment

Abstract

Unmanned aerial vehicles are gaining importance with many civilian and military applications. Especially thesurveillance, 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. Theground users may have no access to the global positioning system (GPS); therefore, their locations have to be estimatedusing alternative techniques. Besides there may be threats in the environment, such as shooters. In this work, we addressthe problem of optimal DBS deployment under the aforementioned constraints. We propose a novel DBS deploymentalgorithm that uses estimated positions of ground users and threats. The proposed algorithm is based on receiver signalstrength-based maximum likelihood estimate of user locations and K-means clustering supported heuristic that takesinto account the positions of threats. Numerical results show that proposed algorithm performs close to the computationintensive near-optimal algorithm and strikes a good trade-off between the number of unserved users and the probabilityof DBSs not being hit.