Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

A Lifetime Maximization Scheme for a Sensor Based MTC Device

dc.contributor.authorAlvi, Sheeraz
dc.contributor.authorZhou, Xiangyun
dc.contributor.authorDurrani, Salman
dc.coverage.spatialAbu Dhabi, United Arab Emirates
dc.date.accessioned2020-02-07T01:46:40Z
dc.date.createdDecember 9-13 2018
dc.date.issued2018-12-09
dc.date.updated2019-11-25T07:29:47Z
dc.description.abstractFor a sensor based machine-type communication (MTC) device, transmission is a power hungry operation and blindly applying too much data compression may even exceed the cost of transmitting raw data, thus losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. We consider a system that is composed of an energy constrained sensor based MTC device and a sink node, and devise an optimal data compression and transmission policy with an objective to maximize the lifetime of the sensor based MTC device whilst satisfying specific delay and bit error rate (BER) constraints when statistical channel gain is known at the sensor node. Our results show that a jointly optimized compression-transmission policy achieves 100% to 1500% better performance as compared to optimizing transmission only without compression under given BER and delay constraints. Importantly, the gain is most profound in the low latency regime.en_AU
dc.description.sponsorshipThis work was supported by the Australian Research Council’s Discovery Project Funding Scheme (Project number DP170100939).en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn9781538647271en_AU
dc.identifier.issn1930-529Xen_AU
dc.identifier.urihttp://hdl.handle.net/1885/201528
dc.language.isoen_AUen_AU
dc.publisherIEEEen_AU
dc.relationhttp://purl.org/au-research/grants/arc/DP170100939en_AU
dc.relation.ispartofseries2018 IEEE Global Communications Conference, GLOBECOM 2018
dc.rights© 2018 IEEEen_AU
dc.source2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedingsen_AU
dc.subjectMachine-type communicationen_AU
dc.subjectlifetimeen_AU
dc.subjectdata compressionen_AU
dc.subjectdata transmissionen_AU
dc.subjectenergy efficiencyen_AU
dc.titleA Lifetime Maximization Scheme for a Sensor Based MTC Deviceen_AU
dc.typeConference paperen_AU
local.contributor.affiliationAlvi, Sheeraz, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationZhou, Xiangyun (Sean), College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationDurrani, Salman, College of Engineering and Computer Science, ANUen_AU
local.contributor.authoruidAlvi, Sheeraz, u5943663en_AU
local.contributor.authoruidZhou, Xiangyun (Sean), u2586105en_AU
local.contributor.authoruidDurrani, Salman, u4243008en_AU
local.description.embargo2037-12-31
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor100510 - Wireless Communicationsen_AU
local.identifier.absfor090609 - Signal Processingen_AU
local.identifier.absseo970109 - Expanding Knowledge in Engineeringen_AU
local.identifier.ariespublicationu3102795xPUB1171en_AU
local.identifier.doi10.1109/GLOCOM.2018.8647869en_AU
local.identifier.essn2576-6813en_AU
local.identifier.scopusID2-s2.0-85056548885
local.publisher.urlhttps://ieeexplore.ieee.orgen_AU
local.type.statusPublished Versionen_AU

Downloads

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
01_Alvi_A_Lifetime_Maximization_Scheme_2019.pdf
Size:
185.33 KB
Format:
Adobe Portable Document Format