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Sensors 2015, 15(8), 18613-18640; doi:10.3390/s150818613

Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience

Dipartimento di Informatica—Scienza e Ingegneria (DISI), University of Bologna, Viale Risorgimento, 2, 40126 Bologna, Italy
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Author to whom correspondence should be addressed.
Academic Editor: Antonio Puliafito
Received: 16 May 2015 / Revised: 19 July 2015 / Accepted: 24 July 2015 / Published: 30 July 2015
(This article belongs to the Special Issue Sensors and Smart Cities)
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Abstract

Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City areas of interest. This paper proposes an in-depth study of the challenging technical issues related to the efficient assignment of Mobile Crowd Sensing (MCS) data collection tasks to volunteers in a crowdsensing campaign. In particular, the paper originally describes how to increase the effectiveness of the proposed sensing campaigns through the inclusion of several new facilities, including accurate participant selection algorithms able to profile and predict user mobility patterns, gaming techniques, and timely geo-notification. The reported results show the feasibility of exploiting profiling trends/prediction techniques from volunteers’ behavior; moreover, they quantitatively compare different MCS task assignment strategies based on large-scale and real MCS data campaigns run in the ParticipAct living lab, an ongoing MCS real-world experiment that involved more than 170 students of the University of Bologna for more than one year. View Full-Text
Keywords: mobile crowd sensing; mobile crowd datasets; smart cities; task assignment strategies; performance evaluation mobile crowd sensing; mobile crowd datasets; smart cities; task assignment strategies; performance evaluation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bellavista, P.; Corradi, A.; Foschini, L.; Ianniello, R. Scalable and Cost-Effective Assignment of Mobile Crowdsensing Tasks Based on Profiling Trends and Prediction: The ParticipAct Living Lab Experience. Sensors 2015, 15, 18613-18640.

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