Traffic-aware Gateway Placement for High-capacity Flying Networks

Coelho, André; Fontes, Helder; Campos, Rui; Ricardo, Manuel

doi:10.1109/vtc2021-spring51267.2021.9448966

Cited by 13 publications

(22 citation statements)

References 16 publications

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“…When it comes to the FCR UAV positioning challenge, Coelho et al 23 proposed a traffic‐aware positioning algorithm for flying networks with controlled topology. The proposed algorithm considers the traffic generated by each of the FAPs to define the position of the FCR, which acts as a flying gateway between the FAPs and the Internet.…”

Section: Related Workmentioning

confidence: 99%

“…Building upon the conclusion that hovering is not the most power‐efficient state, 22 EREP defines the trajectory and speed of a rotary‐wing FCR UAV that minimizes its energy consumption for propulsion, without compromising the QoS offered by the flying network. EREP represents a step forward with respect to the algorithm proposed by Coelho et al, 23 which defines the positioning of an FCR UAV from the network performance point of view, without considering the energy consumption of the UAV.…”

Section: Introductionmentioning

confidence: 99%

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Energy‐aware relay positioning in flying networks

Rodrigues

Coelho

Ricardo

et al. 2022

Int J Communication

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Summary The ability to move and hover has made rotary‐wing unmanned aerial vehicles (UAVs) suitable platforms to act as flying communications relays (FCRs), aiming at providing on‐demand, temporary wireless connectivity when there is no network infrastructure available or a need to reinforce the capacity of existing networks. However, since UAVs rely on their on‐board batteries, which can be drained quickly, they typically need to land frequently for recharging or replacing them, limiting their endurance and the flying network availability. The problem is exacerbated when a single FCR UAV is used. The FCR UAV energy is used for two main tasks: Communications and propulsion. The literature has been focused on optimizing both the flying network performance and energy efficiency from the communications point of view, overlooking 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 this article, we propose energy‐aware relay positioning (EREP), an algorithm for positioning the FCR taking into account the energy spent for the UAV propulsion. Building upon the conclusion that hovering is not the most energy‐efficient state, EREP defines the trajectory and speed that minimize the energy spent by the FCR UAV on propulsion, without compromising in practice the quality of service offered by the flying network. The EREP algorithm is evaluated using simulations. The obtained results show gains up to 26% in the FCR UAV endurance for negligible throughput and delay degradation.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy‐aware relay positioning in flying networks

Rodrigues

Coelho

Ricardo

et al. 2022

Int J Communication

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“…The GPQM algorithm results from the combination of the GWP and PQM algorithms that we have originally proposed in [19] and [27], respectively. Building upon the conclusion that the usage of static, oversized queues in IEEE 802.11 networks leads to underutilized communications nodes and unnecessary high delays without improving the aggregate throughput [46], GPQM aims at periodically: 1) defining the FGW position, in order to enable a wireless link between each F AP i and the FGW that meets the traffic demand of F AP i ; and 2) calculating the queue size for each F AP i , in order to provide stochastic delay guarantees.…”

Section: Traffic-aware Gateway Placement and Queue Management (Gpqm) ...mentioning

confidence: 99%

“…This aspect has been overlooked in the state of the art, where an unconstrained backhaul network is commonly considered [3]. In order to address this challenge, in [19] we have proposed a centralized traffic-aware gateway placement (GWP) algorithm for flying networks with controlled topology. GWP considers the information about the traffic demand and the future positions of the FAPs to enable backhaul communications paths with high enough capacity to accommodate the offered traffic.…”

Section: Introductionmentioning

confidence: 99%

“…A traffic-aware gateway placement and queue management algorithm for flying networks, called GPQM. GPQM results from the combination of the GWP [19] and PQM [27] algorithms, maximizing the aggregate throughput and providing stochastic delay guarantees. GPQM complements both algorithms by proactively defining at periodic time instants 1) the FGW position, in order to enable wireless links between the FAPs and the FGW with high enough capacity to transport the offered traffic, and 2) the queue size for each FAP, in order to provide stochastic delay guarantees.…”

Section: Introductionmentioning

confidence: 99%

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Traffic-aware Gateway Placement and Queue Management in Flying Networks

Coelho¹,

Campos²,

Ricardo³

2022

Preprint

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Unmanned Aerial Vehicles (UAVs) have emerged as adequate platforms to carry communications nodes, including Wi-Fi Access Points and cellular Base Stations. This has led to the concept of flying networks composed of UAVs as a flexible and agile solution to provide on-demand wireless connectivity anytime, anywhere. However, state of the art works have been focused on optimizing the placement of the access network providing connectivity to ground users, overlooking the backhaul network design. In order to improve the overall Quality of Service (QoS) offered to ground users, the placement of Flying Gateways (FGWs) and the size of the queues configured in the UAVs need to be carefully defined to meet strict performance requirements. The main contribution of this article is a traffic-aware gateway placement and queue management (GPQM) algorithm for flying networks. GPQM takes advantage of knowing in advance the positions of the UAVs and their traffic demand to determine the FGW position and the queue size of the UAVs, in order to maximize the aggregate throughput and provide stochastic delay guarantees. GPQM is evaluated by means of ns-3 simulations, considering a realistic wireless channel model. The results demonstrate significant gains in the QoS offered when GPQM is used.

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