Employing Intelligent Aerial Data Aggregators for Internet of Things: Challenges and Solutions

Li, Kai; Ni, W.; Noor, Alam; Guizani, Mohsen

Publication

Employing Intelligent Aerial Data Aggregators for Internet of Things: Challenges and Solutions

2022-04-01Journal article

dc.contributor.author	Li, Kai
dc.contributor.author	Ni, W.
dc.contributor.author	Noor, Alam
dc.contributor.author	Guizani, Mohsen
dc.date.accessioned	2022-10-03T13:23:13Z
dc.date.available	2022-10-03T13:23:13Z
dc.date.issued	2022-04-01
dc.description.abstract	Internet-of-Things (IoT) devices equipped with temperature and humidity sensors, and cameras are increasingly deployed to monitor remote and human-unfriendly areas, e.g., farmlands, forests, rural highways or electricity infrastructures. Aerial data aggregators, e.g., autonomous drones, provide a promising solution for collecting sensory data of the IoT devices in human-unfriendly environments, enhancing network scalability and connectivity. The flexibility of a drone and favourable line-of-sight connection between the drone and IoT devices can be exploited to improve data reception at the drone. This article first discusses challenges of the drone-assisted data aggregation in IoT networks, such as incomplete network knowledge at the drone, limited buffers of the IoT devices, and lossy wireless channels. Next, we investigate the feasibility of onboard deep reinforcement learning-based solutions to allow a drone to learn its cruise control and data collection schedule online. For deep reinforcement learning in a continuous operation domain, deep deterministic policy gradient (DDPG) is suitable to deliver effective joint cruise control and communication decision, using its outdated knowledge of the IoT devices and network states. A case study shows that the DDPG-based framework can take advantage of the continuous actions to substantially outperform existing non-learning-based alternatives.	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/IOTM.001.2100161	pt_PT
dc.identifier.uri	http://hdl.handle.net/10400.22/20901
dc.language.iso	eng	pt_PT
dc.publisher	IEEE	pt_PT
dc.relation	UIDP/UIDB/04234/2020	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	pt_PT
dc.subject	Autonomous Drone	pt_PT
dc.subject	Internet of Things	pt_PT
dc.subject	Data aggregation	pt_PT
dc.subject	Cruise control	pt_PT
dc.subject	Deep reinforcement learning	pt_PT
dc.title	Employing Intelligent Aerial Data Aggregators for Internet of Things: Challenges and Solutions	pt_PT
dc.title.alternative	220401	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	1	pt_PT
oaire.citation.title	IEEE Internet of Things Magazine	pt_PT
oaire.citation.volume	5	pt_PT
oaire.fundingStream	3599-PPCDT
person.familyName	Li
person.familyName	NOOR
person.givenName	Kai
person.givenName	ALAM
person.identifier.ciencia-id	EE10-B822-16ED
person.identifier.ciencia-id	F919-244E-A2A5
person.identifier.orcid	0000-0002-0517-2392
person.identifier.orcid	0000-0002-0077-6509
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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