11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

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

  • @INPROCEEDINGS{10.4108/icst.mobiquitous.2014.257986,
        author={Nuno Cruz and Hugo Miranda},
        title={MobIPLity: A trace-based mobility scenario generator for mobile applications},
        proceedings={11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={ACM},
        proceedings_a={MOBIQUITOUS},
        year={2014},
        month={11},
        keywords={mobility models mobile applications mobility traces simulation},
        doi={10.4108/icst.mobiquitous.2014.257986}
    }
    
  • Nuno Cruz
    Hugo Miranda
    Year: 2014
    MobIPLity: A trace-based mobility scenario generator for mobile applications
    MOBIQUITOUS
    ICST
    DOI: 10.4108/icst.mobiquitous.2014.257986
Nuno Cruz1,*, Hugo Miranda2
  • 1: ISEL/IPL - LaSIGE/FC/UL
  • 2: Universidade de Lisboa - Faculdade de Ciências - LaSIGE
*Contact email: ncruz-ec@net.ipl.pt

Abstract

The understanding of human mobility patterns is key for the development and evaluation of ubiquitous applications. To circumvent the scarcity and difficulties in capturing mobility data, a number of models has been devised. The accuracy in replicating observed human mobility by these models varies. In general, each model concentrates in replicating some of the metrics that have been observed, while neglecting others. Unfortunately, all tend to neglect diversity, in the roles and goals of the users but also in the devices that are used to access the wireless network.

This paper presents the mobility traces that could be extracted from the access records of 49000 devices on the 190+ access points of the eduroam WiFi network on the campus of the Lisbon Polytechnic Institute between 2005 and 2012. The traces are made publicly available in the expectation that its large scale permits to support evaluations based exclusively on real mobility data, thus removing the uncertainty that emerges from the use of synthetic mobility models. Traces emphasise the differences that can be found between device types, with impact on aspects like the observed trace duration, speed, pause times, ICTs and availability and which can hardly be replicated on synthetic mobility models.