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.
|Publication status||Published - 12 Nov 2017|
|Event||NIPS 2017 Workshop on Optimization: 10th NIPS Workshop on Optimization for Machine Learning - Long Beach, Long Beach, United States|
Duration: 8 Dec 2017 → …
Conference number: 10
|Workshop||NIPS 2017 Workshop on Optimization|
|Period||8/12/17 → …|
Zhang, P., Nevat, I., Peters, G. W., Fruehwirt, W., Huang, Y., Anders, I., & Osborne, M. (2017). Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World. Paper presented at NIPS 2017 Workshop on Optimization, Long Beach, United States.