SmartProbe: A Bottleneck Capacity Estimation Tool for Smartphones (Contributo in atti di convegno)

Type
Label
  • SmartProbe: A Bottleneck Capacity Estimation Tool for Smartphones (Contributo in atti di convegno) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/GreenCom-iThings-CPSCom.2013.371 (literal)
Alternative label
  • Francesco Disperati, Dario Grassini, Enrico Gregori, Alessandro Improta, Luciano Lenzini, Davide Pellegrino, Nilo Redini (2013)
    SmartProbe: A Bottleneck Capacity Estimation Tool for Smartphones
    in 2013 IEEE International Conference on Green Computing and Communications (GreenCom) and IEEE Internet of Things(iThings) and IEEE Cyber, Physical and Social Computing(CPSCom), Beijing, China
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Francesco Disperati, Dario Grassini, Enrico Gregori, Alessandro Improta, Luciano Lenzini, Davide Pellegrino, Nilo Redini (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • IIT-CNR, Università di Pisa (literal)
Titolo
  • SmartProbe: A Bottleneck Capacity Estimation Tool for Smartphones (literal)
Abstract
  • The recent development of Internet cellular and wireless networks led several mobile phone manufacturers to create smartphones able to connect to the Internet anytime-anywhere. Despite the growing dependency of mobile phones from the Internet, very little efforts have been done to measure wireless network performances from smartphones. In this paper, we introduce SmartProbe, a tool dedicated to smartphones able to discover the bottleneck link capacity between two hosts. This tool is based on the technique proposed in PBProbe, but uses a lower amount of resources, making it appealing to the smartphone environment, where the battery consumption and costs are topics of extreme importance. Thanks to the capability of smartphones to understand the type of network where they are connected and to the technique exploited to choose the correct size of the probing packet train, Smart probe is able to save on average more than 80% of data if compared to the original technique depicted in PBProbe, still maintaining a comparable precision. (literal)
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