Participatory sensing (PS) has gained significant attention as
a crowdsourcing methodology that allows ordinary citizens
(non-expert contributors) to collect data using low-cost mobile
devices. In particular, it has been useful in the collection
of environmental data. However, current PS applications
suffer from two problems. First, they do not coordinate the
measurements taken by their users, which is required to maximise
system efficiency. Second, they are vulnerable to malicious
behaviour. In this context, we propose a novel algorithm
that simultaneously addresses both of these problems. Specifically,
we use heteroskedastic Gaussian Processes to incorporate
users’ trustworthiness into a Bayesian spatio-temporal
regression model. The model is trained with measurements
taken by participants, thus it is able to estimate the value of
the phenomenon at any spatio-temporal location of interest
and also learn the level of trustworthiness of each user. Given
this model, the coordination system is able to make informed
decisions concerning when, where and who should take measurements
over a period of time. We empirically evaluate our
algorithm on a real-world human mobility and air quality
dataset, where malicious behaviour is synthetically produced,
and show that our algorithm outperforms the current state of
the art by up to 60.4% in terms of RMSE while having a reasonable
runtime.