# Auction-based Charging Scheduling with Deep Learning Framework for Multi-Drone Networks

arXiv: https://arxiv.org/abs/2001.03611 · HTML: https://mlsanity.com/paper/2001.03611/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 12 of 12)

- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control](https://mlsanity.com/paper/2201.05843/) (2022)
- [Quantum Neural Networks: Concepts, Applications, and Challenges](https://mlsanity.com/paper/2108.01468/) (2021)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control](https://mlsanity.com/paper/2210.00945/) (2022)
- [Trends in Neural Architecture Search: Towards the Acceleration of Search](https://mlsanity.com/paper/2108.08474/) (2021)
- [Neural Myerson Auction for Truthful and Energy-Efficient Autonomous Aerial Data Delivery](https://mlsanity.com/paper/2201.01170/) (2021)
- [Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications](https://mlsanity.com/paper/2304.08493/) (2022)
- [A Survey of Online Auction Mechanism Design Using Deep Learning Approaches](https://mlsanity.com/paper/2110.06880/) (2021)
- [Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks](https://mlsanity.com/paper/2111.11856/) (2021)
- [Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification](https://mlsanity.com/paper/2206.02098/) (2022)
- [Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks](https://mlsanity.com/paper/2209.00862/) (2022)
- [Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning](https://mlsanity.com/paper/2211.15375/) (2022)
- [Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes](https://mlsanity.com/paper/2307.09711/) (2023)

## Abstract (arXiv)

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learning-based approach provides effective battery charging control in multi-drone scenarios.