TY - GEN
T1 - Where Things Roam
T2 - 20th ACM Internet Measurement Conference, IMC 2020
AU - Lutu, Andra
AU - Jun, Byungjin
AU - Finamore, Alessandro
AU - Bustamante, Fabián E.
AU - Perino, Diego
N1 - Funding Information:
We thank the IMC anonymous reviewers, and our shepherd, Nina Taft, for their helpful comments and guidance. We also thank Daniel Hidalgo Pazos (Telefonica Business Solutions) for his invaluable help collecting and analyzing the dataset from the M2M platform; and Javad Kangosstar (O2 UK) for his continued support while processing the MNO’s dataset. The work of Andra Lutu was supported by the EC H2020 Marie Curie Individual Fellowship 841315 (DICE).
Publisher Copyright:
© 2020 ACM.
PY - 2020/10/27
Y1 - 2020/10/27
N2 - Support for "things"roaming internationally has become critical for Internet of Things (IoT) verticals, from connected cars to smart meters and wearables, and explains the commercial success of Machine-to-Machine (M2M) platforms. We analyze IoT verticals operating with connectivity via IoT SIMs, and present the first large-scale study of commercially deployed IoT SIMs for energy meters. We also present the first characterization of an operational M2M platform and the first analysis of the rather opaque associated ecosystem. For operators, the exponential growth of IoT has meant increased stress on the infrastructure shared with traditional roaming traffic. Our analysis quantifies the adoption of roaming by M2M platforms and the impact they have on the underlying visited Mobile Network Operators (MNOs). To manage the impact of massive deployments of device operating with an IoT SIM, operators must be able to distinguish between the latter and traditional inbound roamers. We build a comprehensive dataset capturing the device population of a large European MNO over three weeks. With this, we propose and validate a classification approach that can allow operators to distinguish inbound roaming IoT devices.
AB - Support for "things"roaming internationally has become critical for Internet of Things (IoT) verticals, from connected cars to smart meters and wearables, and explains the commercial success of Machine-to-Machine (M2M) platforms. We analyze IoT verticals operating with connectivity via IoT SIMs, and present the first large-scale study of commercially deployed IoT SIMs for energy meters. We also present the first characterization of an operational M2M platform and the first analysis of the rather opaque associated ecosystem. For operators, the exponential growth of IoT has meant increased stress on the infrastructure shared with traditional roaming traffic. Our analysis quantifies the adoption of roaming by M2M platforms and the impact they have on the underlying visited Mobile Network Operators (MNOs). To manage the impact of massive deployments of device operating with an IoT SIM, operators must be able to distinguish between the latter and traditional inbound roamers. We build a comprehensive dataset capturing the device population of a large European MNO over three weeks. With this, we propose and validate a classification approach that can allow operators to distinguish inbound roaming IoT devices.
KW - Cellular M2M services
KW - IoT Roaming
KW - Traffic analysis
UR - http://www.scopus.com/inward/record.url?scp=85097265986&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097265986&partnerID=8YFLogxK
U2 - 10.1145/3419394.3423661
DO - 10.1145/3419394.3423661
M3 - Conference contribution
AN - SCOPUS:85097265986
T3 - Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
SP - 147
EP - 161
BT - IMC 2020 - Proceedings of the 2020 ACM Internet Measurement Conference
PB - Association for Computing Machinery
Y2 - 27 October 2020 through 29 October 2020
ER -
