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Johnsson, Andreas
Publications (1 of 1) Show all publications
Yan, W., Flinta, C. & Johnsson, A. (2019). Machine-Learning Based Active Measurement Proxy for IoT Systems. In: 2019 IFIP/IEEE Symposium On Integrated Network And Service Management (IM): . Paper presented at IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Arlington, VA. April 08-12, 2019 (pp. 198-206). IEEE
Open this publication in new window or tab >>Machine-Learning Based Active Measurement Proxy for IoT Systems
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
Keywords
Active Measurements, IoT, Machine Learning, Wireless Networks, Network Management
National Category
Communication Systems
Identifiers
urn:nbn:se:uu:diva-387992 (URN)000469937200050 ()978-3-903176-15-7 (ISBN)
Conference
IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Arlington, VA. April 08-12, 2019
Funder
Swedish Foundation for Strategic Research , SM15-0026
Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-11-27
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