Evolutionary deployment and local search-based movements of 0th responders in disaster scenarios

The establishment of communications in disaster scenarios is of paramount importance, especially because preexisting communication infrastructure is likely to be destroyed or malfunctioning. Consequently, there is a need for an alternative and self-organizing communication infrastructure that can be rapidly deployed in disaster situations. In this paper, we propose to use drones or unmanned aerial vehicles as 0th responders to form a network that provides communication services to victims. Finding the best positions of the 0th responders is a non-trivial problem and is, therefore, divided into two phases. The first phase is the initial deployment, where the 0th responders are placed using partial information on the disaster scenario. In the second phase, which we call the adaptation to real conditions, the drones move according to a local search algorithm to find positions that provide better coverage to the victims. We conduct extensive simulations to validate our proposed approach for rural disaster scenarios under different conditions. We show that our proposed initial deployment based on genetic algorithm provides coverage for up to 94% (maximum) and 86% (mean) of victims if complete knowledge of the disaster scenario is known and 10 drones are used. When the adaptation to the real condition phase is used, this percentage is increased to 95% (maximum). If no knowledge of the scenario and 10 UAVs are used 80% (maximum) and 59% (mean) of victims are found and successfully covered. The proposed approach outperforms in 6.4% the random deployment method, and in 2.4% the best grid deployment approach. Finally, we show that by using different numbers of drones for the two phases of the proposed approach, the percentage of victims is increased up to 51% for low values of knowledge of the scenario.