Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as suitable platforms for
transporting and positioning communications nodes on demand, including
Wi-Fi Access Points and cellular Base Stations. This paved the way for
the deployment of flying networks capable of temporarily providing
wireless connectivity and reinforcing the coverage and capacity of
existing networks anywhere, anytime. Several solutions have been
proposed in the literature for the positioning of UAVs that act as
Flying Access Points (FAPs). Yet, the positioning of Flying
Communications Relays (FCRs) in charge of forwarding the traffic to/from
the Internet has not received equal attention. A major challenge in
flying networks is the UAVs endurance. Since the UAVs are typically
powered by on-board batteries with limited capacity, whose energy is
used for communications and propulsion, the UAVs need to land frequently
for recharging or replacing their batteries, limiting the flying network
availability. State of the art works are focused on optimizing both the
flying network performance and the energy-efficiency from the
communications point of view, but do not consider the energy spent for
the UAV propulsion. Yet, the energy spent for communications is
typically negligible when compared with the energy spent for the UAV
propulsion.
In order to address the FCR UAV positioning and energy-efficiency
challenges, we have proposed the Energy-aware RElay Positioning (EREP)
algorithm. EREP defines the trajectory and speed of the FCR UAV that
minimize the energy spent for the UAV propulsion. However, since EREP
considers a theoretical radio propagation model for computing the
minimum Signal-to-Noise Radio (SNR) values that allow to meet the FAPs
traffic demand, this may lead to network performance degradation in
real-world networking scenarios, especially due to the FCR UAV movement.
In this article, we propose the Energy and Performance Aware relay
Positioning (EPAP) algorithm. Built upon the EREP algorithm, EPAP
defines target performance-aware SNR values for the wireless links
established between the FCR UAV and the FAPs and, based on that,
computes the trajectory to be completed by the FCR UAV, so that the
energy spent for the UAV propulsion is minimized. EPAP was evaluated in
terms of both the flying network performance and the FCR UAV endurance,
considering multiple networking scenarios. Simulation results show gains
up to 25% in the FCR UAV endurance, while not compromising the Quality
of Service offered by the flying network.