Abstract
Users’ personal information is one of the most important actives for nowadays enterprises. Knowing user preferences allows to offer personalized interactions and obtain more high-value information. In this context, crowdsensing shows as a technique that aims to collect information about the users and their Internet of Everything (IoE) environment. Personal smartphones are the devices that act as the interface between people and the IoE. However, most of the related works in the literature consider smartphones as mere sensors that gather user data, which is then transferred to the crowdsensing requester. As an alternative, in this paper we propose a distributed crowdsensing platform based on a extension of the Digital Avatars framework for Mobile Collaborative Social Computing. In our proposal, smartphones are responsible for compiling and keeping the digital avatar or virtual profile of each of the users participating in the crowdsensing activity. Based on these avatars, the framework is extended to provide a distributed platform for both the dissemination and the aggregation of the results of the activity, granting users with privacy and ownership of their personal data. Our proposal also takes into account trust and user reputation by means of subjective logic. The proposed system is tested and validated through a proof of concept.
This work has been funded under the Spanish research projects RTI2018-098780-B-I00, which funds the PRE2019-089614 predoctoral contract, PID2021-125527NB-I00, and TED2021-130523B-I00.
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Notes
- 1.
Public repository of the proposal: https://github.com/apvereda/UCAmI2023-CrowdSensing.git
- 2.
BeanShell: https://github.com/beanshell/beanshell
- 3.
OneSignal: https://onesignal.com/.
- 4.
Public repository of the proposal: https://github.com/apvereda/UCAmI2023-CrowdSensing.git.
- 5.
Android Profiler: https://developer.android.com/studio/profile/android-profiler?hl=es-419
- 6.
Battery Historian: https://github.com/google/battery-historian
- 7.
Proposal repository: https://github.com/apvereda/UCAmI2023-CrowdSensing.git
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Perez-Vereda, A., Cabañero, L., Moreno, N., Hervas, R., Canal, C. (2023). Distributed Crowdsensing Based on Mobile Personal Data Stores. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 841. Springer, Cham. https://doi.org/10.1007/978-3-031-48590-9_1
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