2014
DOI: 10.1007/s11276-014-0790-2
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Analysing social behaviour and message dissemination in human based delay tolerant network

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Cited by 15 publications
(9 citation statements)
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“…Some studies have shown that many popular mobility models, such as random waypoint, random walk, and community-based model, have such a characteristic that the pair-wise inter-contact time and contact duration are exponentially distributed or have exponential tails [Spyropoulos, Psounis and Raghavendra (2006); Batabyal and Bhaumik (2015)]. In this paper, we propose a method to estimate utility based on the hypothesis that the pair-wise inter-contact time and contact duration between nodes are independent random variables and follow exponential distribution.…”
Section: System Analysismentioning
confidence: 99%
“…Some studies have shown that many popular mobility models, such as random waypoint, random walk, and community-based model, have such a characteristic that the pair-wise inter-contact time and contact duration are exponentially distributed or have exponential tails [Spyropoulos, Psounis and Raghavendra (2006); Batabyal and Bhaumik (2015)]. In this paper, we propose a method to estimate utility based on the hypothesis that the pair-wise inter-contact time and contact duration between nodes are independent random variables and follow exponential distribution.…”
Section: System Analysismentioning
confidence: 99%
“…Otherwise message is forwarded based on higher global centrality value and finally it is delivered to one of the members of its community or destination. This scheme may not work when node belongs to the community whose all members are with low global centrality value [21]. Friendship routing [18] construct friendship community using the social pressure metric which measures the quality of friendship based on their long lasting and regular contact.…”
Section: Related Workmentioning
confidence: 99%
“…As community structure changes over the time which is not considered in the existing techniques of community detection. S. Batyabal et al [21] proposed a method of community detection by exploiting the distribution of ICTs and CTs from the mobility traces. An aggregated ICTs and CTs of all node pairs follow power law distribution with exponential cut off [28][29].…”
Section: Related Workmentioning
confidence: 99%
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