toplogo
Sign In

Optimizing UAV Fleets for Prioritized Data Harvesting: A Cross-Layer Approach


Core Concepts
The core message of this paper is to develop a cross-layer optimization framework for orchestrating a fleet of MIMO-capable rotary-wing UAVs to efficiently harvest prioritized traffic from a random distribution of MIMO-capable heterogeneous users, by optimizing the beam-forming design, the 3D UAV positioning and trajectory, and the user association/scheduling policy.
Abstract
This paper presents a cross-layer optimization framework for orchestrating a fleet of MIMO-capable rotary-wing Unmanned Aerial Vehicles (UAVs) to harvest prioritized traffic from a random distribution of MIMO-capable heterogeneous users. The key highlights and insights are: The authors model a deployment scenario with a finite-horizon offline setting, where a fleet of U UAVs needs to harvest prioritized traffic from G ground nodes (GNs) with varying quality-of-service requirements. The authors develop a probabilistic air-to-ground channel model, a multi-user MIMO uplink communication model with prioritized traffic, and a novel 3D mobility power consumption model for rotary-wing UAVs. The authors formulate the fleet-wide reward maximization problem and decompose it into decoupled sub-problems, solving for the optimal positioning of the UAVs, their energy-conscious 3D trajectories, the beam-forming design, and the user association/scheduling policy. For the UAV positioning, the authors employ a two-stage grid search coupled with zero-forcing beam-forming. For the 3D trajectory design, they use a learning-based competitive swarm optimization algorithm under an average power constraint. For the user association/scheduling, they formulate a multiple traveling salesman problem and solve it using a graphical branch-and-bound method. The numerical evaluations demonstrate that the proposed solution outperforms static UAV deployments, adaptive Voronoi decomposition techniques, and state-of-the-art iterative fleet control algorithms, in terms of user quality-of-service and per-UAV average power consumption.
Stats
The average link throughput between a GN g and its serving UAV u is given by: ¯Rug (dug, θug) = E[¯Rug (dug, θug, Λ)] The reward Ωug obtained by UAV u for harvesting the data from GN g is given by: Ωug = χgγ(δug−δg,max) g where δug = νg/¯Rug (dug, θug)
Quotes
"Crucially, unlike the formulation in this paper, the approaches that solve for UAV-assisted data harvesting [8]–[10] fail to model user requests with varied priority levels vis-á-vis their quality-of-service requirements." "Contrary to the optimization perspective presented in this work, in addition to not modeling prioritized traffic, the solutions in [11]–[15] do not account for the on-board energy constraints of the UAVs, while designing their optimal trajectories."

Key Insights Distilled From

by Bharath Kesh... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00961.pdf
Orchestrating UAVs for Prioritized Data Harvesting

Deeper Inquiries

How can the proposed cross-layer optimization framework be extended to handle dynamic user arrivals and departures during the mission execution

To extend the proposed cross-layer optimization framework to handle dynamic user arrivals and departures during mission execution, several adjustments and additions can be made. Firstly, the clustering algorithm used for grouping users could be updated in real-time to accommodate new arrivals or departures. This would involve continuously monitoring the user distribution and dynamically reassigning users to clusters as needed. Additionally, the trajectory planning algorithm for UAVs would need to be adaptive to account for changes in the number and distribution of users. The scheduling policy would also require real-time updates to ensure efficient allocation of resources to new users while maintaining service quality for existing users. By incorporating mechanisms for dynamic user management, the framework can effectively handle changing user scenarios during mission execution.

What are the potential challenges and trade-offs in incorporating additional constraints, such as collision avoidance between UAVs and obstacles in the environment, into the optimization problem

Incorporating additional constraints, such as collision avoidance between UAVs and obstacles in the environment, into the optimization problem introduces both challenges and trade-offs. One challenge is the increased complexity of the optimization problem due to the need to consider obstacle detection and avoidance in real-time. This complexity can impact the computational efficiency of the solution and may require advanced algorithms for collision prediction and avoidance. Trade-offs may arise between optimizing for maximum data harvesting efficiency and ensuring safe UAV operations. Balancing these trade-offs involves finding a solution that minimizes collision risks while still achieving the desired data harvesting objectives. Implementing collision avoidance constraints may also limit the flexibility of UAV trajectories, potentially affecting the overall performance of the system.

How can the insights from this work on prioritized data harvesting be applied to other UAV-enabled applications, such as emergency response or infrastructure inspection, where the timely delivery of critical information is paramount

The insights gained from prioritized data harvesting in the context of UAV fleet orchestration can be applied to other UAV-enabled applications where timely delivery of critical information is crucial, such as emergency response or infrastructure inspection. In emergency response scenarios, the framework's focus on prioritizing user requests based on quality-of-service requirements can be invaluable for efficiently allocating resources to tasks with varying levels of urgency. By adapting the optimization framework to prioritize emergency data collection and delivery, response times can be minimized, leading to more effective emergency management. Similarly, in infrastructure inspection applications, the framework's emphasis on maximizing fleet-wide reward while considering constraints like power consumption can enhance the efficiency of inspection tasks. By tailoring the framework to prioritize inspection tasks based on criticality and optimizing UAV trajectories for comprehensive coverage, infrastructure inspections can be conducted more effectively and with reduced operational costs.
0