Document - Understanding human mobility for CrowdSensing strategies with the ParticipAct data set

2020

Conference article Open Access

Understanding human mobility for CrowdSensing strategies with the ParticipAct data set

Chessa S., Foschini L., Girolami M.

Mobility Analysis Mobility analysis Mobile CrowdSensing

The Mobile CrowdSensing (MCS) paradigm has been increasingly adopted in the last years. Its adoption has been proved as beneficial for different scenarios, such as environmental monitoring and mobility analysis. However, one of the major barriers of the MCS initiatives, is the difficulty in recruiting users for the purpose of collecting data. We focus in this work to such limitation, and we analyze the mobility traces collected with a real-world MCS experiment, namely ParticipAct. Our goal is to discuss how to exploit the mobility features of the recruited users, as grounding information to plan and optimize a MCS data collection campaign. In detail, we analyze the quality of the data set, its accuracy and several features of human mobility such as radius of gyration and the real entropy of the locations visited. We discuss the impact of such metrics on the task scheduling, allocation and how to obtain a certain Tcoverage of data from visited locations.

Source: GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, 07-11/12/2020

Publisher: IEEE COMPUTER SOC, LOS ALAMITOS, CA, USA

Metrics

Back to previous page

Cite as

BibTeX entry

@inproceedings{oai:it.cnr:prodotti:443675,
	title = {Understanding human mobility for CrowdSensing strategies with the ParticipAct data set},
	author = {Chessa S. and Foschini L. and Girolami M.},
	publisher = {IEEE COMPUTER SOC, LOS ALAMITOS, CA, USA},
	doi = {10.1109/globecom42002.2020.9322541},
	booktitle = {GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, 07-11/12/2020},
	year = {2020}
}