Deep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networks

Li, K; Ni, W; Tovar, E; Jamalipour, A

Deep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networks

Li, K Ni, W

Tovar, E Jamalipour, A

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, 2020-June, pp. 1-6
Issue Date:: 2020-07-27

Closed Access

	Filename	Description	Size
	09149282.pdf	Published version	241.12 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Li, K
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Tovar, E
dc.contributor.author	Jamalipour, A
dc.date	2020-06-07
dc.date.accessioned	2021-04-14T21:22:13Z
dc.date.available	2021-04-14T21:22:13Z
dc.date.issued	2020-07-27
dc.identifier.citation	ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, 2020-June, pp. 1-6
dc.identifier.isbn	978-1-7281-5089-5
dc.identifier.issn	1550-3607
dc.identifier.issn	1938-1883
dc.identifier.uri	http://hdl.handle.net/10453/148113
dc.description.abstract	In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
dc.relation.ispartof	IEEE International Conference on Communications
dc.relation.isbasedon	10.1109/icc40277.2020.9149282
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networks
dc.type	Conference Proceeding
utslib.citation.volume	2020-June
utslib.location.activity	Dublin, Ireland
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-14T21:22:12Z
pubs.finish-date	2020-06-11
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-06-07
pubs.volume	2020-June
dc.location	Piscataway, USA

Abstract:

In Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148113