
handle: 11585/599532
New sensors embedded into modern smartphones has led into a new data collection prospective in which people directly collect all the sensitive data. This feature has found different applications, in particular in the Smart Cities area, in order to establish dynamic communications between the citizens and the city government. This category of application is nestled into the Mobile Crowd Sensing (MCS) application group, due to their final purpose of sharing sensing data to an open platform that includes a huge number of people. This paper presents an extension of the general-purpose ParticipAct platform, a MCS application developed by the University of Bologna, focused on the needs of people with impaired mobility. The goal is specializing ParticipAct to enable a crowdsourcing platform that guarantees a solid support for their lifetime allowing reviewing and sharing opinions regarding public and private places and architectonic barriers of a city area. Showed results confirm the effectiveness of the developed application in terms of both its viability via integration with existing and widely diffused Geographical Information Systems (GIS), and its feasibility in terms of system and user-perceived performances.
Big Data; Crowdsourcing; Map; Mobile App; Software; Signal Processing; Mathematics (all); Computer Science Applications1707 Computer Vision and Pattern Recognition; Computer Networks and Communications
Big Data; Crowdsourcing; Map; Mobile App; Software; Signal Processing; Mathematics (all); Computer Science Applications1707 Computer Vision and Pattern Recognition; Computer Networks and Communications
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 15 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
