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Full Length Article | Open Access

GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks

Pratik Thantharate(), Anurag Thantharate, Atul Kulkarni
School of Computing and Engineering, University of Missouri, Kansas City, MO, USA
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HIGHLIGHTS

● The proposed method aims to maximize the utilization of recharging at Base Stations (BSs) while minimizing the travel distance of UAVs to address the limited onboard energy of UAVs.

● The model uses Mixed Integer Linear Programming to optimize UAV energy management by considering stationary/mobile UAVs, battery life, flight time, and optimal paths to charging stations.

● The results of the model show a significant reduction in energy expenditure compared to a heuristic solution, with an objective value of 49.8 compared to 54.8.

● The proposed model increases UAV network efficiency by exploiting charging capabilities of base stations and supercharging stations, enabling longer flight times and lower energy usage.

Graphical Abstract

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Abstract

Unmanned Aerial Vehicles (UAVs) offer a strategic solution to address the increasing demand for cellular connectivity in rural, remote, and disaster-hit regions lacking traditional infrastructure. However, UAVs’ limited onboard energy storage necessitates optimized, energy-efficient communication strategies and intelligent energy expenditure to maximize productivity. This work proposes a novel joint optimization model to coordinate charging operations across multiple UAVs functioning as aerial base stations. The model optimizes charging station assignments and trajectories to maximize UAV flight time and minimize overall energy expenditure. By leveraging both static ground base stations and mobile supercharging stations for opportunistic charging while considering battery chemistry constraints, the mixed integer linear programming approach reduces energy usage by 9.1 ​% versus conventional greedy heuristics. The key results provide insights into separating charging strategies based on UAV mobility patterns, fully utilizing all available infrastructure through balanced distribution, and strategically leveraging existing base stations before deploying dedicated charging assets. Compared to myopic localized decisions, the globally optimized solution extends battery life and enhances productivity. Overall, this work marks a significant advance in UAV energy management by consolidating multiple improvements within a unified coordination framework focused on joint charging optimization across UAV fleets. The model lays a critical foundation for energy-efficient aerial network deployments to serve the connectivity needs of the future.

Keywords

5G new radioUnmanned aerial vehicles (UAV)UAV battery lifeEnergy efficiencyUnmanned aircraft systems (UAS)UAV schedulingCellular networksLinear programmingJoint UAV charging optimization

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Green Energy and Intelligent Transportation
Volume 3 Issue 1,
February 2024
Article number: 100130
DOI: 10.1016/j.geits.2023.100130
Cite this article:
Thantharate P, Thantharate A, Kulkarni A. GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks. Green Energy and Intelligent Transportation, 2024, 3(1): 100130. https://doi.org/10.1016/j.geits.2023.100130
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