Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent
paradigms for urban sensing. The citizens actively participate in the sensing process by contributing
data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed
for sensing and reporting operations. Hence, devising energy efficient data collection frameworks
(DCF) is essential to foster participation. In this work, we investigate from an energy-perspective
the performance of different DCFs. Our methodology is as follows: (i) we developed an Android
application that implements the DCFs, (ii) we profiled the energy and network performance with a
power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for
large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The
amount of collected data, energy consumption and fairness are the performance indexes evaluated.
The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than
DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to
produce the same amount of data, the associated energy cost of different users can vary significantly
