4 research outputs found
Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks
Spatial regression of random fields based on potentially biased sensing
information is proposed in this paper. One major concern in such applications
is that since it is not known a-priori what the accuracy of the collected data
from each sensor is, the performance can be negatively affected if the
collected information is not fused appropriately. For example, the data
collector may measure the phenomenon inappropriately, or alternatively, the
sensors could be out of calibration, thus introducing random gain and bias to
the measurement process. Such readings would be systematically distorted,
leading to incorrect estimation of the spatial field. To combat this
detrimental effect, we develop a robust version of the spatial field model
based on a mixture of Gaussian process experts. We then develop two different
approaches for Bayesian spatial field reconstruction: the first algorithm is
the Spatial Best Linear Unbiased Estimator (S-BLUE), in which one considers the
quadratic loss function and restricts the estimator to the linear family of
transformations; the second algorithm is based on empirical Bayes, which
utilises a two-stage estimation procedure to produce accurate predictive
inference in the presence of "misbehaving" sensors. In addition, we develop the
distributed version of these two approaches to drastically improve the
computational efficiency in large-scale settings. We present extensive
simulation results using both synthetic datasets and semi-synthetic datasets
with real temperature measurements and simulated distortions to draw useful
conclusions regarding the performance of each of the algorithms
Channel Prediction with Location Uncertainty for Ad-Hoc Networks
Multi-agent systems (MAS) rely on positioning technologies to determine their physical location, and on wireless communication technologies to exchange information. Both positioning and communication are affected by uncertainties, which should be accounted for. This paper considers an agent placement problem to optimize end-to-end communication quality in a MAS, in the presence of uncertainties. Using Gaussian processes (GPs), operating on the input space of location distributions, we are able to model, learn, and predict the wireless channel. Predictions, in the form of distributions, are fed into the communication optimization problems. This approach inherently avoids regions of the workspace with high position uncertainty and leads to better average communication performance. We illustrate the benefits of our approach via extensive simulations, based on real wireless channel measurements. Finally, we demonstrate the improved channel learning and prediction performance, as well as the increased robustness in agent placement