期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 12, 页码 9787-9799出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3019186
关键词
Communication decisions; deep reinforcement learning; flight cruise control; Internet of Things (IoT); unmanned aerial vehicles (UAVs)
资金
- FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit [UIDB/04234/2020]
- Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement, through the European Regional Development Fund
- FCT [POCI-01-0145-FEDER-029074]
- Fundação para a Ciência e a Tecnologia [UIDB/04234/2020] Funding Source: FCT
This article proposes using unmanned aerial vehicles as aerial data collectors in Internet-of-Things networks to minimize network data loss. By formulating the problem as a partially observable Markov decision process and utilizing a deep Q-network based flight resource allocation scheme, significant reductions in packet loss were achieved compared to existing nonlearning heuristics.
Employing unmanned aerial vehicles (UAVs) as aerial data collectors in Internet-of-Things (IoT) networks is a promising technology for large-scale environment sensing. A key challenge in UAV-aided data collection is that UAV maneuvering gives rise to buffer overflow at the IoT node and unsuccessful transmission due to lossy airborne channels. This article formulates a joint optimization of flight cruise control and data collection schedule to minimize network data loss as a partially observable Markov decision process (POMDP), where the states of individual IoT nodes can be obscure to the UAV. The problem can be optimally solvable by reinforcement learning, but suffers from the curse of dimensionality and becomes rapidly intractable with the growth in the number of IoT nodes. In practice, a UAV-aided IoT network contains a large number of network states and actions in POMDP while the up-to-date knowledge is not available at the UAV. We propose an onboard deep Q-network-based flight resource allocation scheme (DQN-FRAS) to optimize the online flight cruise control of the UAV and data scheduling given outdated knowledge on the network states. Numerical results demonstrate that DQN-FRAS reduces the packet loss by over 51%, as compared to existing nonlearning heuristics.
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