Energy-efficient and solar powered mission planning of UAV swarms to reduce the coverage gap in rural areas: The 3D case

Abstract Although the percentage of people living outside a broadband network has more than halved in recent years, around 10% of the global population does not have access to the Internet. This lack of coverage is particularly concentrated in rural and low-income areas, in which the lack of a cost-effective electricity supply is the main barrier to expanding network coverage. To tackle this problem, this work proposes a theoretical model based on a self-sustainable 5G network architecture in which the mission planning of a swarm of Unmanned Aerial Vehicles (UAVs) is efficiently scheduled to provide cellular coverage over the territory and to reduce the required energy consumption. A Mixed Integer Linear Programming is provided to formalize the problem of minimizing the energy consumption required by the swarm of UAVs that are able to provide coverage by operating at different altitudes. In order to practically solve the problem, a Genetic Algorithm is defined and evaluated over a realistic scenario. Results indicate that a higher granularity in the number of altitudes at which UAVs can provide coverage increases the percentage of territory that is covered, while a small penalty on the energy consumption must be paid compared to the case in which an unique altitude over the ground is considered.