Social opportunistic networks (SONs) are intermittently-connected networks that exploit unpredictable contacts between users' mobile devices. The connectivity of SONs exhibits a nonrandom structure with the existence of a few hub nodes and social-aware routing protocols favour these nodes as the best carriers for message transfers. As a result, the network suffers from unbalanced traffic distribution leading to traffic congestion in the hub nodes. In this thesis, strategies for improving traffic distribution fairness in SONs and reducing traffic congestion in hub nodes are considered. The thesis proposes three innovative contributions as follows. An Analysis of traffic distribution and network capacity in SONs is fIrst performed. It considers the traffic distribution in a SON, and characterizes the network as being scale-free. Several forwarding strategies are considered, based on the routing information required by a node, i.e. isolated, local and complete networks. A network capacity model for a SON is then derived as an upper-bound of network delivery performance, where hub nodes' resources become the limiting factor. The analytical study shows that unfair traffic distribution occurs in the network for all three forwarding strategies, because a few hub nodes process a large fraction of the traffic. A Traffic Distribution Aware (TraDA) routing protocol is therefore proposed, aimed at improving traffic distribution fairness in SONs. A novel computation of node global popularity (the TraDA routing metric) is proposed, comprising node intrinsic popularity and social-relations popularity calculations. TraDa-Comm, a community-aware variant of TraDA, is also presented and simulation results show that it achieves better traffic distribution fairness than the state-of-the-art Bubble Rap, without degrading network delivery performance.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:616477 |
| Date | January 2014 |
| Creators | Soelistijanto, Bambang |
| Publisher | University of Surrey |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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