Distributed Gaussian process regression for mobile sensor networks under localization uncertainty

In this paper, we propose distributed Gaussian process regression for resource-constrained mobile sensor networks under localization uncertainty. The proposed distributed algorithm, which combines Jacobi over-relaxation (JOR) and discrete-time average consensus (DAC), can effectively handle localization uncertainty as well as limited communication ranges and computation capabilities of mobile sensor networks. The performance of the proposed method is verified in numerical simulations against the centralized maximum a posteriori solution and the quick-and-dirty solution. We show that the proposed method outperforms the quick-and-dirty solution and achieves an accuracy comparable to the centralized solution.