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.
Highlights
The large availability of mobile devices with sensing capabilities, combined with the pervasive spread of communication infrastructures, gave rise, in recent years, to a number of platforms for MobileCrowd Sensing (MCS)
Mobile Crowd Sensing (MCS) solutions could be of extreme importance from the community/smart city managers perspective because they can enable the monitoring of areas that are still not covered by fixed monitoring infrastructures
That is the motivation of the state machine in Figure 1: only relevant task state transitions call for synchronization; in particular, transitions to available, refused, ignored, running, succeeded, and failed states are transparently synchronized with the server in the ParticipAct platform; if one of those transitions occurs when there is no data connectivity, the task state is implemented via a soft state to be automatically finalized as soon as the server acknowledges it
Summary
The large availability of mobile devices with sensing capabilities, combined with the pervasive spread of communication infrastructures, gave rise, in recent years, to a number of platforms for Mobile. The boundary between social and technical challenges is not clear cut: for example, the minimization of the global resource overhead by considering a minimal subset of users in a crowdsensing campaign requires analyzing geo-social profiles, identifying and inferring which users are most likely to successfully harvest the required data These differentiated MCS strategies and policies, toward overall MCS optimization, are very important; while some first efforts in the past have addressed these issues with some initial theoretic discussions and simulative approaches [4], only a few seminal efforts used real and over-the-city MCS living labs, with the goal to draw consistent guidelines and lessons about the feasibility and effectiveness of employed optimization solutions in real MCS testbeds. An analysis of related work (Section 6) and an assessment of the current state of ParticipAct as well as of its future goals conclude the article
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