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Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance
RISE SICS, Stockholm, Sweden.
RISE SICS, Stockholm, Sweden.
RISE SICS, Stockholm, Sweden.
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).ORCID-id: 0000-0001-7311-9334
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2018 (Engelska)Ingår i: 2018 NETWORK TRAFFIC MEASUREMENT AND ANALYSIS CONFERENCE (TMA), IEEE, 2018Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Connected vehicles can make roads traffic safer and more efficient, but require the mobile networks to handle time-critical applications. Using the MONROE mobile broadband measurement testbed we conduct a multi-access measurement study on buses. The objective is to understand what network performance connected vehicles can expect in today's mobile networks, in terms of transaction times and availability. The goal is also to understand to what extent access to several operators in parallel can improve communication performance. In our measurement experiments we repeatedly transfer warning messages from moving buses to a stationary server. We triplicate the messages and always perform three transactions in parallel over three different cellular operators. This creates a dataset with which we can compare the operators in an objective way and with which we can study the potential for multi-access. In this paper we use the triple-access dataset to evaluate single-access selection strategies, where one operator is chosen for each transaction. We show that if we have access to three operators and for each transaction choose the operator with best access technology and best signal quality then we can significantly improve availability and transaction times compared to the individual operators. The median transaction time improves with 6% compared to the best single operator and with 61% compared to the worst single operator. The 90-percentile transaction time improves with 23% compared to the best single operator and with 65% compared to the worst single operator.

Ort, förlag, år, upplaga, sidor
IEEE, 2018.
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-71054ISI: 000454696100027Scopus ID: 2-s2.0-85057241219OAI: oai:DiVA.org:kau-71054DiVA, id: diva2:1288753
Konferens
2nd Network Traffic Measurement and Analysis Conference (TMA)Vienna, Austria JUN 26-29, 2018
Tillgänglig från: 2019-02-14 Skapad: 2019-02-14 Senast uppdaterad: 2019-11-13Bibliografiskt granskad

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Brunström, Anna

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