UAVs Deployment in Disaster Scenarios Based on Global and Local Search Optimization Algorithms

Abstract

The advancements in Unmanned Aerial Vehicle (UAV) related technologies and wireless communications pave the way for the deployment of wireless mesh networks in the air. These air mesh networks can be suitable for providing communication services in disaster scenarios to ground nodes such as victims and first responders. However, the optimal deployment of UAVs is not an easy as the number of possible scenarios to position the UAVs may reach a computationally challenging level. The combination of global and local search optimization algorithms can be considered as a powerful way for dealing with the massive number of possible solutions. We propose a deployment approach based on a global search algorithm such as the genetic algorithm and a local search algorithm namely the hill climbing algorithm. We show that the combination of both optimization techniques provides promising results for optimal positioning of UAVs in disaster scenarios based on simulation examples.