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Spatial Gaussian process regression with mobile sensor networks.

Dongbing Gu, Huosheng Hu

IEEE Transactions on Neural Networks and Learning Systems

|May 9, 2014

Summary

This study introduces a distributed Gaussian process regression (DGPR) for mobile wireless sensor networks. This method enables independent node operation and adaptive spatiotemporal function modeling.

Area of Science:

Computer Science
Electrical Engineering
Robotics

Background:

Mobile wireless sensor networks (MWSNs) require efficient methods for modeling spatial and spatiotemporal functions.
Existing methods may face challenges with distributed computation and adaptability in dynamic environments.

Purpose of the Study:

To develop a distributed Gaussian process regression (DGPR) approach for MWSNs.
To enable independent regression computations at each sensor node.
To facilitate adaptive modeling of spatiotemporal functions in mobile networks.

Main Methods:

Utilized sparse Gaussian process regression with a compactly supported covariance function.
Developed a neighbor-to-neighbor communication protocol for distributed computation.

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Integrated an information entropy-based locational optimization algorithm for collective motion control.

Main Results:

The DGPR approach allows independent regression results from each sensor node.
The method effectively models both stationary spatial functions and dynamic spatiotemporal functions.
Simulations demonstrated the approach's performance and adaptability.

Conclusions:

The proposed DGPR method offers an efficient and scalable solution for MWSNs.
The approach enhances the network's ability to adapt to changing environments and functions.
DGPR facilitates intelligent collective motion control through distributed processing.