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Opportunistic data collection for IoT-based indoor air quality monitoring
conference contribution
posted on 2017-01-01, 00:00 authored by A Zhalgasbekova, Arkady ZaslavskyArkady Zaslavsky, S Saguna, K Mitra, P P JayaramanOpportunistic sensing advance methods of IoT data collection using the mobility of data mules, the proximity of transmitting sensor devices and cost efficiency to decide when, where, how and at what cost collect IoT data and deliver it to a sink. This paper proposes, develops, implements and evaluates the algorithm called CollMule which builds on and extends the 3D kNN approach to discover, negotiate, collect and deliver the sensed data in an energy- and cost-efficient manner. The developed CollMule software prototype uses Android platform to handle indoor air quality data from heterogeneous IoT devices. The CollMule evaluation is based on performing rate, power consumption and CPU usage of single algorithm cycle. The outcomes of these experiments prove the feasibility of CollMule use on mobile smart devices.
History
Event
IEEE Communications Society Russia Northwest Chapter. Conference (2017 : St. Petersburg, Russia)Volume
LNCS 10531Series
IEEE Communications Society Russia Northwest Chapter ConferencePagination
53 - 65Publisher
SpringerLocation
St. Petersburg, RussiaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2017-08-28End date
2017-08-30ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319673790Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2017, Springer International Publishing AGEditor/Contributor(s)
O Galinina, S Andreev, S Balandin, Y KoucheryavyTitle of proceedings
ruSMART 2017, NsCC 2017, NEW2AN 2017 : Proceedings of the 2017 10th Conference on Internet of Things and Smart Spaces, the Third International Workshop on Nano-scale Computing and Communications and the 17th International Conference on Next Generation Wired/Wireless NetworkingUsage metrics
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