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Machine-Learning Based Active Measurement Proxy for IoT Systems
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorarkitektur och datorkommunikation.
Ericsson Res, Stockholm, Sweden.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Datorteknik. Ericsson Res, Stockholm, Sweden.
2019 (engelsk)Inngår i: 2019 IFIP/IEEE Symposium On Integrated Network And Service Management (IM), IEEE, 2019, s. 198-206Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Network operators are accustomed to using IP-layer active measurements for assessing end-to-end network performance and expect that new technology, such as IoT, provides similar means. Unfortunately, active measurements in IoT systems are associated with both energy and network overhead. This paper presents and evaluates a novel active-measurement proxy approach, based on machine learning, that enables reduction of active measurement overhead in IoT systems. The paper describes the approach and its implementation. Further, the approach is evaluated in a IEEE 802.15.4 testbed, and the results show high-performing and accurate modeling.

sted, utgiver, år, opplag, sider
IEEE, 2019. s. 198-206
Emneord [en]
Active Measurements, IoT, Machine Learning, Wireless Networks, Network Management
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-387992ISI: 000469937200050ISBN: 978-3-903176-15-7 (digital)OAI: oai:DiVA.org:uu-387992DiVA, id: diva2:1331979
Konferanse
IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Arlington, VA. April 08-12, 2019
Forskningsfinansiär
Swedish Foundation for Strategic Research , SM15-0026Tilgjengelig fra: 2019-06-27 Laget: 2019-06-27 Sist oppdatert: 2019-11-27

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Totalt: 39 treff
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