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Machine-Learning Based Active Measurement Proxy for IoT Systems
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication.
Ericsson Res, Stockholm, Sweden.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. Ericsson Res, Stockholm, Sweden.
2019 (English)In: 2019 IFIP/IEEE Symposium On Integrated Network And Service Management (IM), IEEE, 2019, p. 198-206Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2019. p. 198-206
Keywords [en]
Active Measurements, IoT, Machine Learning, Wireless Networks, Network Management
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:uu:diva-387992ISI: 000469937200050ISBN: 978-3-903176-15-7 (electronic)OAI: oai:DiVA.org:uu-387992DiVA, id: diva2:1331979
Conference
IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Arlington, VA. April 08-12, 2019
Funder
Swedish Foundation for Strategic Research , SM15-0026Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-11-27

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Yan, WenqingJohnsson, Andreas

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