Publication
Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
| dc.contributor.author | Li, Kai | |
| dc.contributor.author | Ni, Wei | |
| dc.contributor.author | Emami, Yousef | |
| dc.contributor.author | Dressler, Falko | |
| dc.date.accessioned | 2023-01-18T11:51:07Z | |
| dc.date.available | 2023-01-18T11:51:07Z | |
| dc.date.issued | 2022-05-06 | |
| dc.description.abstract | Energy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes. | pt_PT |
| dc.description.sponsorship | This work was supported in part by the National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit under Grant UIDP/UIDB/04234/2020, and in part by the National Funds through FCT, under CMU Portugal Partnership under Project CMU/TIC/0022/2019 (CRUAV). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1109/MWC.002.2100681 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/21642 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.relation | UIDP/UIDB/04234/2020 | pt_PT |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9920736 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Internet of drones | pt_PT |
| dc.subject | Multi-agent deep reinforcement learning | pt_PT |
| dc.subject | Flight control | pt_PT |
| dc.subject | Data aggregation | pt_PT |
| dc.subject | Long short-term memory | pt_PT |
| dc.title | Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning | pt_PT |
| dc.title.alternative | 220502 | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/3599-PPCDT/CMU%2FTIC%2F0022%2F2019/PT | |
| oaire.citation.issue | 4 | pt_PT |
| oaire.citation.title | IEEE Wireless Communications | pt_PT |
| oaire.citation.volume | 29 | pt_PT |
| oaire.fundingStream | 3599-PPCDT | |
| person.familyName | Li | |
| person.familyName | emami | |
| person.givenName | Kai | |
| person.givenName | yousef | |
| person.identifier.ciencia-id | EE10-B822-16ED | |
| person.identifier.orcid | 0000-0002-0517-2392 | |
| person.identifier.orcid | 0000-0002-8842-2616 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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