A Trust-based Mixture of Gaussian Processes Model for
Reliable Regression in Participatory Sensing
Abstract
Data trustworthiness is a crucial issue in real-world
participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks,
can result in biased and inaccurate estimation and
decision making. We propose a novel trust-based
mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior
and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based
algorithm to efficiently perform Bayesian inference
of the model. Experiments using two real-world
datasets show the superior robustness of our model
compared with existing approaches