MobIPLity: A trace-based mobility scenario generator for mobile applications

Cruz, Nuno; Miranda, Hugo

doi:10.4108/icst.mobiquitous.2014.257986

Cited by 2 publications

(5 citation statements)

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“…In particular, information extracted using standard SQL commands contributed to diversify data' applications, which include discussions on the evolution of the utilisation of wireless networks and mobile devices, the generation of mobility scenarios and the study of the recurrence of contacts. The interested reader is referred to [11] for a more in depth analysis of the data set and to [9] for its applications on the generation of mobility scenarios.…”

Section: Methodsmentioning

confidence: 99%

“…This paper extends these results by demonstrating their applicability in a practical scenario using a reputation system as the supporting application. The eduroam data collected in the scope of this project was further applied in the development of Mobiplity, 1 a publicly available scenario generator for human mobility using exclusively real data [9].…”

Section: Related Workmentioning

confidence: 99%

“…The ranking algorithm was experimented using a mobility scenario generated by MobIPLity [9] with all devices that connected to the eduroam network on IPL during the year of 2013. MobIPLity uses the records produced by the RADIUS service to create mobility scenarios that closely reflect observed user behaviour.…”

Section: Trace Generation On Mobiplitymentioning

confidence: 99%

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Recurring Contacts between Groups of Devices: Analysis and Application

Cruz

Miranda

2018

IEEE Trans. on Mobile Comput.

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The capability to anticipate a contact with another device can contribute to improving the performance and user satisfaction of mobile social network applications and of any other relying on some form of data harvesting or hoarding. This paper presents a nine year data set of wireless access logs produced by more than 70,000 devices and 40,000 users. Research on the recurring contact patterns observed between groups of devices permitted to model the probabilities of occurrence of a contact at a predefined date between pairs of devices. As an example, the paper presents and evaluates an algorithm that provides daily contact predictions, based on the history of past pairwise contacts and its application on a reputation service.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Recurring Contacts between Groups of Devices: Analysis and Application

Cruz

Miranda

2018

IEEE Trans. on Mobile Comput.

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“…Sessions with these characteristics are created when a user has some issue while connecting to the network, although the network considers the user authenticated (thus creating the RADIUS record). The interested reader is referred to [9] for a more in depth analysis of the dataset and to [8] for its applications on the generation of mobility scenarios.…”

Section: Methodsmentioning

confidence: 99%

“…The ranking algorithm was experimented using a mobility scenario generated by MobIPLity [8] with all devices that connected to the eduroam network on IPL during the year of 2013. MobIPLity is a framework that produces mobility scenarios in bonnmotion [1] format.…”

Section: Evaluation In Mobiplitymentioning

confidence: 99%

Recurring contact opportunities within groups of devices

Cruz¹,

Miranda²

2015

Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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The capability to anticipate a contact with another device can greatly improve the performance and user satisfaction not only of mobile social network applications but of any other relying on some form of data harvesting or hoarding. One of the most promising approaches for contact prediction is to extrapolate from past experiences. This paper investigates the recurring contact patterns observed between groups of devices using an 8-year dataset of wireless access logs produced by more than 70000 devices. This effort permitted to model the probabilities of occurrence of a contact at a predefined date between groups of devices using a power law distribution that varies according to neighbourhood size and recurrence period. In the general case, the model can be used by applications that need to disseminate large datasets by groups of devices. As an example, the paper presents and evaluates an algorithm that provides daily contact predictions, based on the history of past pairwise contacts and their duration.

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MobIPLity: A trace-based mobility scenario generator for mobile applications

Cited by 2 publications

References 0 publications

Recurring Contacts between Groups of Devices: Analysis and Application

Recurring Contacts between Groups of Devices: Analysis and Application

Recurring contact opportunities within groups of devices

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