2010
DOI: 10.1109/tmc.2010.99
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Power Law and Exponential Decay of Intercontact Times between Mobile Devices

Abstract: We examine the fundamental properties that determine the basic performance metrics for opportunistic communications. We first consider the distribution of inter-contact times between mobile devices. Using a diverse set of measured mobility traces, we find as an invariant property that there is a characteristic time, order of half a day, beyond which the distribution decays exponentially. Up to this value, the distribution in many cases follows a power law, as shown in recent work. This power law finding was pr… Show more

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Cited by 305 publications
(191 citation statements)
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“…For two nodes from two different communities, we consider that the intercontact time follows a bounded power law distribution ([30mins, 7 days]) with the slope equal to 1.2, resulting in the average interaction frequency about 1 per day. The settings here are close to those obtained from the real traces [19,23,29]. We first demonstrate the trust evaluation performance assuming there is sufficient storage space on each node to store the trust values towards all others.…”
Section: Simulation Resultsmentioning
confidence: 67%
See 1 more Smart Citation
“…For two nodes from two different communities, we consider that the intercontact time follows a bounded power law distribution ([30mins, 7 days]) with the slope equal to 1.2, resulting in the average interaction frequency about 1 per day. The settings here are close to those obtained from the real traces [19,23,29]. We first demonstrate the trust evaluation performance assuming there is sufficient storage space on each node to store the trust values towards all others.…”
Section: Simulation Resultsmentioning
confidence: 67%
“…The initial trust value of all devices is set to ignorance (0.5). We assume the encounter or interaction pattern follows the power-law distribution (with or without exponential cutoff) which is supported by the analysis of many real traces [19,23,29]. For two nodes in the same CoI, we consider that the inter-contact time follows a bounded power law distribution ([10mins, 2 days]) with the slope equal to 1.4, resulting in the average interaction frequency about 6 times per day.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…The fruitful result of this research is that social aware DTN constructs a transmission strategy that is based on community distribution and centrality. This research adopts distributed K-Clique algorithm to conduct community distribution [35] and uses singlewindow centrality to calculate whole centrality and partial centrality. To control copy overhead, this research proposes a dichotomy approach.…”
Section: Discussionmentioning
confidence: 99%
“…Since each BSN computes the same weight values independently, each BSN will choose the same sensor classifier. However, if there are multiple sensor classifiers with the same weight, the BSN with the lowest BSN ID value chooses a sensor and broadcasts its choice to neighboring BSNs (lines [14][15][16][17]. If a private sensor (sensor a user does not share with neighbors) classifier has the highest weight, it is chosen along with one other public sensor classifier to ensure all BSNs choose the same classifier (lines 6-11).…”
Section: Collaborative Sensor Selectionmentioning
confidence: 99%