Promoting congestion control in opportunistic networks

Grundy, Andrew; Radenkovic, Milena

doi:10.1109/wimob.2010.5645048

Cited by 22 publications

(34 citation statements)

References 20 publications

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“…centrality and tie strength, has failed to provide a proper solution for traffic balancing. Hence, we agree with the authors of [10,11] that both Fair Route and CAFé need an additional strategy, e.g. (storage) congestion control, which reduces the traffic in hub nodes.…”

Section: Related Worksupporting

confidence: 89%

“…The authors in [10] investigated the drawback of SimBet in term of traffic distribution in the network and then proposed Fair Route to address the issue. CAFé [11] has also been proposed with the aim of distributing load away from hub nodes. The approach adopted by both Fair Route and CAFé is to reduce the effect of centrality by smoothing its value (in CAFé) or decreasing it in time (in Fair Route).…”

Section: Related Workmentioning

confidence: 99%

“…Interaction strength, i.e. some measure of the strength of a link between two nodes, was also added in the forwarding metrics of [10,11] to improve the algorithm performance. Similarly, the authors of SimBet [7] improved their existing algorithm by adding tie strength in the routing metric.…”

Section: Related Workmentioning

confidence: 99%

“…This improved algorithm, SimbetTS [12], combines the frequency of contact, contact duration and the age of contact as measures of the tie strength between two nodes in the network. Unfortunately, it has been shown in [10,11,12] that improving the contact data, i.e. centrality and tie strength, has failed to provide a proper solution for traffic balancing.…”

Section: Related Workmentioning

confidence: 99%

“…For shortest path forwarding, he found Q has the range 1.8 to 2.3. Applying (11) to (9), we are able to approximate the mean arrival rate of traffic in node j as…”

Section: Network Capacity Modelmentioning

confidence: 99%

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Traffic distribution and network capacity analysis in social opportunistic networks

Soelistijanto

Howarth

2012

2012 IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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-Social opportunistic networks are intermittently connected mobile ad hoc networks (ICNs) that exploit human mobility to physically carry messages between disconnected parts of the network. Human mobility thus plays an essential role in the performance of forwarding protocols in the networks, and people's movements are in turn affected by their social interactions with each other. In this paper we present an analysis of the traffic distribution among the nodes of social opportunistic networks and its impact on network capacity. For our analysis, we use a human contact graph that represents a social network of individuals. We characterize the graph as a scale-free network and apply forwarding strategies based on the information required by a node to select relays for its messages, categorising this information either as isolated or complete network or local network knowledge. We use a social network property, centrality, for the forwarding strategies, additionally considering tie strength in the forwarding metric and investigate their impact on traffic distribution. We show that all the strategies result in unfair traffic distribution due to a strong non-random structure of the networks, where hub nodes process much more relay traffic than non-hub nodes. Finally, we present a mathematical model of network capacity as an upper-bound of network delivery performance where hub nodes' resources become the limiting factors, and show that including tie strength in the forwarding metric improves the network capacity.

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Section: Related Worksupporting

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…For shortest path forwarding, he found Q has the range 1.8 to 2.3. Applying (11) to (9), we are able to approximate the mean arrival rate of traffic in node j as…”

Section: Network Capacity Modelmentioning

confidence: 99%

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