Explore the site...


The ebbits project is affiliated with the following programs and organisations:

The ebbits project is active in the FInES cluster, the Future Internet Enterprise Systems (FInES) Cluster, where ebbits is leading the taskforces on international relations and manufacture and industry. Read more here.

The ebbits project is part of the Cluster of European projects on the Internet of Things. The Cluster aims to promote a common vision of the Internet of Things. ebbits is leading the taskforce on semantic interoperability

We Fight Spam


<< February 2018 >>
Mo Tu We Th Fr Sa Su
      1 2 3 4
5 6 7 8 9 10 11
12 13 14 15 16 17 18
19 20 21 22 23 24 25
26 27 28        

No events.


The ebbits project is a 4-year project started in 2010. It is partly funded by the European Commission under the 7th Framework Programme in the area of Internet of Things and Enterprise environments under Grant Agreement no. 257852


 Impressum   Privacy

Share this

Bookmark and Share

Visit us at Facebook

Newsletter Subscription

Registered Members Only

Previous Newsletters

Read previous issues of our newsletter here:
 September 2011
 August 2012
 August 2013
 June 2014
 March 2015

Popular Downloads




Forgotten your password?
Request a new one here.

Downloads: Multi-Access Interface Selection Based on Data Mining Algorithm

Downloads Home > Conference and other scientific publications > Multi-Access Interface Selection Based on Data Mining Algorithm

Multi-Access Interface Selection Based on Data Mining Algorithm

Multi-access networks are envisioned to guarantee a continuous service while addressing different application requirements in terms of data rate, coverage and power consumption. Different access technologies can be effectively combined in a multi-access scenario where the overall system is able to achieve a better performance leveraging on the different characteristics of the access technologies. One of the main challenges in realizing a multi-access system is the process of properly selecting a network interface. This paper proposes a network selection algorithm based on the k-nearest neighbor data mining classification technique. This work considers Ethernet, Wi-Fi, and 3GPP technologies; however, the proposed approach could be easily adapted to take into account other access technologies.
02 August 2013 12:22
Download 556
License: Purchase IEEE Xplore
O/S: pdf

Download Stats Downloads: 100
Downloaded: 94406
Most Downloaded: D5.1.1 Concept and Technologies in Intelligent Service Structures 1.pdf [ 4126 ]
Most Recent: Brochure [ 995 ]