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Publicação
Deep Q-Networks for Aerial Data Collection in Multi-UAV-Assisted Wireless Sensor Networks
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Orientador(es)
Resumo(s)
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 multiagent 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.
Descrição
Palavras-chave
Unmanned aerial vehicles Communication scheduling Multi-UAV Deep Reinforcement Learning Deep QNetwork