  Mobile crowdsensing allows data collection at a scale and pace that was once
impossible. One of the biggest challenges in mobile crowdsensing is that
participants may exhibit malicious or unreliable behavior. Therefore, it
becomes imperative to design algorithms to accurately classify between reliable
and unreliable sensing reports. To this end, we propose a novel Framework for
optimizing Information Reliability in Smartphone-based participaTory sensing
(FIRST), that leverages mobile trusted participants (MTPs) to securely assess
the reliability of sensing reports. FIRST models and solves the challenging
problem of determining before deployment the minimum number of MTPs to be used
in order to achieve desired classification accuracy. We extensively evaluate
FIRST through an implementation in iOS and Android of a room occupancy
monitoring system, and through simulations with real-world mobility traces.
Experimental results demonstrate that FIRST reduces significantly the impact of
three security attacks (i.e., corruption, on/off, and collusion), by achieving
a classification accuracy of almost 80% in the considered scenarios. Finally,
we discuss our ongoing research efforts to test the performance of FIRST as
part of the National Map Corps project.
