Joint traffic-aware UAV placement and predictive routing for aerial networks

Almeida, Eduardo Nuno; Coelho, André; Ruela, José; Campos, Rui; Ricardo, Manuel

doi:10.1016/j.adhoc.2021.102525

Cited by 25 publications

(25 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the ns-3 simulator includes a set of theoretical propagation models tailored for wireless networks, in order to improve the accuracy and realism of the performance evaluation presented in this article, the wireless channel between each F AP i and the FGW was characterized based on the experimental model proposed in [55], where it was analyzed in terms of the path-loss and fast-fading components. Regarding the path loss component, it was concluded that the Friis propagation loss model is the best suited to characterize the wireless links in flying networks, as previously suggested in the literature [64].…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…E(t k ) ⊆ V × V is the set of directional wireless links available between U AV i and U AV j at time t k , with (i, j) ∈ E(t k ). The wireless channel between each pair of UAVs is modeled by the Friis propagation loss model [54], which is justified by the strong Line of Sight component that dominates the wireless links established between UAVs [55]. C j,i (t k ) defines the maximum capacity, in bit/s, of the wireless link available between U AV i and U AV j at time t k , considering a constant channel bandwidth B in Hz.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Traffic-aware Gateway Placement and Queue Management in Flying Networks

Coelho¹,

Campos²,

Ricardo³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Evaluation Methodologymentioning

confidence: 99%

Section: System Model and Problem Formulationmentioning

confidence: 99%

Traffic-aware Gateway Placement and Queue Management in Flying Networks

Coelho¹,

Campos²,

Ricardo³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The wireless network is configured in AP-STA infrastructure mode. The FAP-User wireless link is modeled by the Friis path loss and Rician fastfading, according to the experimental channel model for UAVs hovering at low altitudes measured in [2]. Each scenario is characterized by a random combination of users' positions and offered loads, whose sum is always equal to Λ = 40 Mbit/s generated at the application layer.…”

Section: A Training and Performance Evaluation Methodologymentioning

confidence: 99%

“…Therefore, for a given episode e, the objective is to determine a sequence of T FAP positions that maximizes the episode's average network utility N e , as stated in (2).…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…In these heterogeneous scenarios, UAVs can be dynamically positioned according to the users' traffic demands, in order to meet their Quality of Service (QoS) requirements. Despite the ongoing research on UAV placement [2]- [5], some challenges remain open, including the optimization of the UAVs' positions according to the dynamic users' traffic demands.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Traffic-Aware UAV Placement Using a Generalizable Deep Reinforcement Learning Methodology

Almeida¹,

Campos²,

Ricardo³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) acting as Flying Access Points (FAPs) are being used to provide on-demand wireless connectivity in extreme scenarios. Despite ongoing research, the optimization of UAVs' positions according to dynamic users' traffic demands remains challenging. We propose the Trafficaware UAV Placement Algorithm (TUPA), which positions a UAV acting as FAP according to the users' traffic demands, in order to maximize the network utility. Using a DRL approach enables the FAP to autonomously learn and adapt to dynamic conditions and requirements of networking scenarios. Moreover, the proposed DRL methodology allows TUPA to generalize knowledge acquired during training to unknown combinations of users' positions and traffic demands, with no additional training. TUPA is trained and evaluated using network simulator ns-3 and ns3-gym framework. The results demonstrate that TUPA increases the network utility, compared to baseline solutions, increasing the average network utility up to 4x in scenarios with heterogeneous traffic demands.

show abstract

Energy‐aware relay positioning in flying networks

Rodrigues

Coelho

Ricardo

et al. 2022

Int J Communication

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Joint traffic-aware UAV placement and predictive routing for aerial networks

Cited by 25 publications

References 34 publications

Traffic-aware Gateway Placement and Queue Management in Flying Networks

Traffic-aware Gateway Placement and Queue Management in Flying Networks

Traffic-Aware UAV Placement Using a Generalizable Deep Reinforcement Learning Methodology

Energy‐aware relay positioning in flying networks

Contact Info

Product

Resources

About