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Network-Access-Smart-Detection-And-QoE-based-selection-NASDAQ-

Software component

Summary

In the context of heterogeneous and coexisting networks, the introduction of context-aware and cognitive mechanisms may allow the final user to optimize 1) the detection of surrounding access networks, and 2) the selection of the best one, thus improving Quality of Experience (QoE). The present proposal investigates this approach, by introducing the use of 1) MAC layer parameters for the detection of surrounding networks, and 2) application layer QoE parameters, namely Key Performance Indicators (KPIs), for optimizing the access network selection, respectively. MAC parameters and KPIs will be defined for different traffic types, and then used to 1) detect and recognize the access networks in the user surrounding area, 2) rank them, and select the one with highest estimated QoE, for each considered traffic type.

NASDAQ is a tool for QoE-based network selection, in the form of an Android app. At its present implementation, it allows the analysis of the radio environment, in order to detect, identify and rank the WiFi Access Points (APs) available in the area. The app makes possible:

  • the collection (via direct measurement) of QoE parameters, allowing the evaluation of Key Performance Indicators (KPIs), relative to two different traffic types: VoIP and video streaming. At this step, a connection is established with each candidate APs, and the ping utility is used to send test packets (Internet Control Message Protocol echo requests) to a website server. Ping utility offers as a result statistics about the link such as average delay, jitter and packet loss; KPIs are computed using these measured values, together with the link estimated bandwidth.
  • the ranking of the candidate WiFi APs, from the one with highest estimated performance to the one with lowest performance, assigning a score in the 0 – 100 range, obtained as a linear combination of the measured KPIs.

Source code

The source code of the application can be downloaded at https://www.dropbox.com/s/r2m9fx4ijhiie8k/BestNetworkSelector.zip?dl=0 Please cite ouor below-listed papers if you used the source code and the related information.

[1] S. Boldrini, S. Benco, S. Annese, A. Ghettino and M.-G. Di Benedetto, “Bluetooth automatic network recognition – the AIR-AWARE approach”, International Journal of Autonomous and Adaptive Communications Systems, Vol. 7, n. 4, pp. 378-392, 2014.

[2] S. Boldrini, M.-G. Di Benedetto, A. Tosti and J. Fiorina, “Automatic best wireless network selection based on Key Performance Indicators”, In: M.-G. Di Benedetto, A.F. Cattoni, J. Fiorina, F. Bader, L. De Nardis, Cognitive radio and Networking for Heterogeneous Wireless Networks, Springer, ISBN: 978-3-319-01717-4, 2015.

Contact

mariagabriella.dibenedetto@uniroma1.it

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