Recent developments in computation and communication technologies are making big changes to the method in which people communicate with each other. Online social networks, wireless technologies and smart-phones are very common and enable communication to be maintained while people are mobile. These types of communication are closely related to humans because they carry the devices. In this research, we detect, analyse and compare opportunistic networks with human social networks. Currently opportunistic networking platforms have not gone beyond the research and development stage where they are challenging to design and implement. Therefore we develop an indoor mobility tracking system to track the participant movement inside buildings and to record the physical interaction between participants using Bluetooth technology. This system has two different ways of abstracting opportunistic networks from the experimental data: mobility (device-to-building) and co-located (device-to-device) interactions. The mobility detection system has been studied using a volunteer group of students in the School of Computer Science and Informatics. This group also has been studied to understand their social networking structure and characteristics by using electronic survey methodology. Different techniques have been used to investigate the individuals’ networks from the survey and mobility movements and a comparison between them. From a precision and recall technique, we find that 60-80 % of the participants’ social network is embedded in the opportunistic network but a small proportion 10-20% of the opportunistic network is embedded in the social network. This shows the presence of many weak links in the opportunistic network that means the opportunistic network connectivity requires a very small number of key-players to disseminate information throughout the network. We also examine both networks from the perspective of information dissemination. We find that device-to-device create many more weak links to disseminate information rather than server detection. Therefore, information quickly floods throughout the co-located network.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:571745 |
| Date | January 2013 |
| Creators | Ali, Mona |
| Publisher | Cardiff University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://orca.cf.ac.uk/47313/ |
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