Analysis of Dynamic Sensor Networks: Power Law Then What?

Fleury, Éric; Guillaume, Jean‐Loup; Robardet, Céline; Scherrer, Antoine

doi:10.1109/comswa.2007.382427

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…These joint distributions have a significantly different shape for simple random dynamic graph model. This will be discussed in Section 5 (see also [19]). …”

Section: Joint Distributionsmentioning

confidence: 96%

“…Note that is was already shown in [19] that merely forcing the contact and inter-contact duration distributions is not sufficient to fully uncover the dynamics of an experimental data set. From all the possible graph properties that can be simulated by our random dynamical graph generator, mainly two graph properties are emphasized for the sake of the clarity: the number of connected components and the number of connected vertices.…”

Section: Set Of Investigated Modelsmentioning

confidence: 98%

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Description and simulation of dynamic mobility networks

et al. 2008

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International audienceDuring the last decade, the study of large scale complex networks has attracted a substantial amount of attention and works from several domains: sociology, biology, computer science, epidemiology. Most of such complex networks are inherently dynamic, with new vertices and links appearing while some old ones disappear. Until recently, the dynamics of these networks was less studied and there is a strong need for dynamic network models in order to sustain protocol performance evaluations and fundamental analyzes in all the research domains listed above. We propose in this paper a novel framework for the study of dynamic mobility networks. We address the characterization of dynamics by proposing an in-depth description and analysis of two real-world data sets. We show in particular that links creation and deletion processes are independent of other graph properties and that such networks exhibit a large number of possible configurations, from sparse to dense. From those observations, we propose simple yet very accurate models that allow generate random mobility graphs with similar temporal behavior as the one observed in experimental dat

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“…These joint distributions have a significantly different shape for simple random dynamic graph model. This will be discussed in Section 5 (see also [19]). …”

Section: Joint Distributionsmentioning

confidence: 96%

Section: Set Of Investigated Modelsmentioning

confidence: 98%

Description and simulation of dynamic mobility networks

et al. 2008

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Mobility-Assisted Energy-Aware User Contact Detection in Mobile Social Networks

Krishanamurthy

Mohapatra

2013

2013 IEEE 33rd International Conference on Distributed Computing Systems

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Abstract-Many practical problems in mobile social networks such as routing, community detection, and social behavior analysis, rely on accurate user contact detection. The frequently used method for detecting user contact is through Bluetooth on smartphones. However, Bluetooth scans consume lots of power. Although increasing the scan duty cycle can reduce the power consumption, it also reduces the accuracy of contact detection. In this paper, we address this problem based on the observation that user contact changes (i.e., starts and ends of user contacts) are mainly caused by user movement. Since most smartphones have accelerometers, we can use them to detect user movement with much less energy and then start Bluetooth scans to detect user contacts. By conducting experiments on smartphones, we discover three relationships between user movement and user contact changes. According to these relationships, we propose a Mobility-Assisted User Contact detection algorithm (MAUC), which triggers Bluetooth scans only when user movements have a high possibility to cause contact changes. Moreover, we propose energy aware MAUC (E-MAUC) to further reduce energy consumption during Bluetooth discovery, while keeping the same detection accuracy as MAUC. Via trace driven simulations, we show that MAUC can reduce the number of Bluetooth scans by half while maintaining similar contact detection rates compared to existing algorithms, and E-MAUC can further reduce the energy consumption by 45% compared to MAUC.

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SIMPS: Using Sociology for Personal Mobility

Borrel¹,

Legendre²,

Amorim³

et al. 2009

IEEE/ACM Trans. Networking

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Abstract-Assessing mobility in a thorough fashion is a crucial step toward more efficient mobile network design. Recent research on mobility has focused on two main points: analyzing models and studying their impact on data transport. These works investigate the consequences of mobility. In this paper, instead, we focus on the causes of mobility. Starting from established research in sociology, we propose SIMPS, a mobility model of human crowd motion. This model defines two complimentary behaviors, namely socialize and isolate, that regulate an individual with regard to her/his own sociability level. SIMPS leads to results that agree with scaling laws observed both in small-scale and large-scale human motion. Although our model defines only two simple individual behaviors, we observe many emerging collective behaviors (group formation/splitting, path formation, and evolution). To our knowledge, SIMPS is the first model in the networking community that tackles the roots governing mobility.

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Analysis of Dynamic Sensor Networks: Power Law Then What?

Cited by 3 publications

References 19 publications

Description and simulation of dynamic mobility networks

Description and simulation of dynamic mobility networks

Mobility-Assisted Energy-Aware User Contact Detection in Mobile Social Networks

SIMPS: Using Sociology for Personal Mobility

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