Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World

TL;DR

This paper presents methods for spatial field reconstruction and sensor selection in heterogeneous weather sensor networks, enhancing efficiency and performance guarantees for developing world applications.

Contribution

It introduces a low complexity S-BLUE algorithm for spatial reconstruction and a Cross Entropy-based method for sensor set selection with guaranteed performance.

Findings

Efficient spatial field reconstruction using S-BLUE.

Sensor set selection with predictive MSE guarantees.

Applicable to heterogeneous sensor networks in developing regions.

Abstract

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner.