How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing
Abstract
:1. Introduction
- The design of a mobile app able to periodically collect heterogeneous data from mobile devices. In this respect, several technical issues have to be considered, such as the non-intrusiveness of the monitoring app, the management of power consumption, the memory requirements and a broadband connection with a lower bandwidth with respect to modern network links.
- The organization of a user recruitment strategy aiming at involving the maximum number of people and at keeping them engaged during the experiment. This aspect is particularly important as it determines the success of the MCS initiative.
- The heterogeneity of the collected data: the MDC Nokia data set collected several kinds of data covering different human domains: social sensing data, location data, media creation and usage data and behavioural data.
2. A 10-Year Journey in Mobility Analysis
- The number of recruited or monitored users: the number of users varies along with the time, but we consider the maximum or the declared number of users participating in the MCS initiative.
- The duration of the experiment expressed in months: the duration refers to the time elapsed from the first to the last observation.
- The types of data collected: this information describes the number of different data types provided by the data set; hence, it provides a measure of the variety of the collected information. As a representative example, we consider GeoLife, which provides three types of information: timestamp, device coordinates (latitude and longitude) and the altitude (we do not consider the Transportation mode labels in this case, which are not always available, as described in [32,33]).
2.1. Extracting Mobility Features
- The radius of gyration, rg.
- The maximum travelled distances of users, Md.
2.2. Extracting Co-Location Traces from Mobility Traces
3. Finding the Crowd with Community Detection Algorithms
3.1. Spatial-Based Approaches
3.2. Co-Location Based Approaches
4. Usage Scenarios and Beyond
4.1. Contact Tracing and Community Detection
- Identifying communities of users that regularly meet;
- Predicting the next visited community and the location of such meeting.
4.2. Extending the MCS Architecture with an Edge Layer
4.3. Digitalization and Industry 5.0
4.4. Predicting Community Behaviour
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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Girolami, M.; Belli, D.; Chessa, S.; Foschini, L. How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing. Sensors 2021, 21, 6397. https://doi.org/10.3390/s21196397
Girolami M, Belli D, Chessa S, Foschini L. How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing. Sensors. 2021; 21(19):6397. https://doi.org/10.3390/s21196397
Chicago/Turabian StyleGirolami, Michele, Dimitri Belli, Stefano Chessa, and Luca Foschini. 2021. "How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing" Sensors 21, no. 19: 6397. https://doi.org/10.3390/s21196397
APA StyleGirolami, M., Belli, D., Chessa, S., & Foschini, L. (2021). How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing. Sensors, 21(19), 6397. https://doi.org/10.3390/s21196397