A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing
نویسندگان
چکیده
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.
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A Trust-based Mixture of Gaussian Processes Model for Robust Participatory Sensing
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. In this paper, we propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly d...
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