Energy and Age Pareto Optimal Trajectories in UAV-assisted Wireless Data Collection

TL;DR

This paper develops a multi-objective optimization framework for UAV trajectories that balances energy consumption and information freshness, providing Pareto optimal solutions for UAV-assisted wireless data collection.

Contribution

It introduces a novel MILP formulation with Bender's decomposition to efficiently find Pareto optimal UAV paths balancing energy and AoI.

Findings

The Pareto front illustrates the trade-off between energy and AoI.

Non-dominated solutions enable informed decision-making for UAV path planning.

Numerical results validate the effectiveness of the proposed optimization approach.

Abstract

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless network, where a UAV is dispatched to gather information from ground sensor nodes (SN) and transfer the collected data to the depot. The information freshness is captured by the age of information (AoI) metric, whilst the energy consumption of the UAV is seen as another performance criterion. Most importantly, the AoI and energy efficiency are inherently competing metrics, since decreasing the AoI requires the UAV returning to the depot more frequently, leading to a higher energy consumption. To this end, we design UAV paths that optimize these two competing metrics and reveal the Pareto frontier. To formulate this problem, a multi-objective mixed integer linear programming (MILP) is proposed with a flow-based constraint set and we apply Bender's decomposition on the proposed formulation. The overall outcome shows that the proposed method allows deriving non-dominated solutions for decision making for UAV based wireless data collection. Numerical results are provided to corroborate our study by presenting the Pareto front of the two objectives and the effect on the UAV trajectory.