A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing

A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing

Qikun Xiang, Jie Zhang, Ido Nevat, Pengfei Zhang

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3866-3872. https://doi.org/10.24963/ijcai.2017/540

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.
Keywords:
Multidisciplinary Topics and Applications: AI&Security and Privacy
Agent-based and Multi-agent Systems: Trust and Reputation