TY - GEN
T1 - Deep Q-Networks for Aerial Data Collection in Multi-UAV-Assisted Wireless Sensor Networks
AU - Emami, Yousef
AU - Wei, Bo
AU - Li, Kai
AU - Ni, Wei
AU - Tovar, Eduardo
N1 - Funding Information:
This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (CEC/04234); also by national funds through the FCT, under CMU Portugal partnership, within project CMU/TIC/0022/2019 (CRUAV).
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Unmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses. The optimization problem is formulated as a multi-agent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46% and 35% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.
AB - Unmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses. The optimization problem is formulated as a multi-agent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46% and 35% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.
KW - Communication scheduling
KW - Deep Q-Network
KW - Multi-UAV deep reinforcement learning
KW - Unmanned aerial vehicles
UR - http://www.scopus.com/inward/record.url?scp=85125359104&partnerID=8YFLogxK
U2 - 10.1109/IWCMC51323.2021.9498726
DO - 10.1109/IWCMC51323.2021.9498726
M3 - Conference contribution
AN - SCOPUS:85125359104
T3 - 2021 International Wireless Communications and Mobile Computing, IWCMC 2021
SP - 669
EP - 674
BT - 2021 International Wireless Communications and Mobile Computing, IWCMC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Wireless Communications and Mobile Computing, IWCMC 2021
Y2 - 28 June 2021 through 2 July 2021
ER -