Optimal energy efficient level set estimation of spatially-temporally correlated random fields

Zuoen Wang, Jingxian Wu, Jing Yang, Hai Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Level set estimation (LSE) is the process of classifying the region(s) that the values of an unknown function exceed a certain threshold. It has a wide range of applications such as spectrum sensing or environment monitoring. In this paper, we study the the optimal LSE of a linear random field that changes with respect to time. A linear sensor network is used to take discrete samples of the spatially-temporally correlated random field in both the space and time domain, and the sensors operate under a total power constraint. The samples are congregated at a fusion center (FC), which performs LSE of the random field by using the noisy observation of the samples. Under the Gaussian process (GP) framework, we first develop an optimal LSE algorithm that can minimize the LSE error probability. The results are then used to derive the exact LSE error probability with the assistance of frequency domain analysis. The analytical LSE error probability is expressed as an explicit function of a number of system parameters, such as the distance between two adjacent nodes, the sampling period in the time domain, the signal-to-noise ratio (SNR), and the spatial-temporal correlation of the random field. With the analytical results, we can identify the optimum node distance and sampling period that can minimize the LSE error probability.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Communications, ICC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479966646
DOIs
StatePublished - Jul 12 2016
Event2016 IEEE International Conference on Communications, ICC 2016 - Kuala Lumpur, Malaysia
Duration: May 22 2016May 27 2016

Publication series

Name2016 IEEE International Conference on Communications, ICC 2016

Other

Other2016 IEEE International Conference on Communications, ICC 2016
CountryMalaysia
CityKuala Lumpur
Period5/22/165/27/16

Fingerprint

Sampling
Frequency domain analysis
Sensor networks
Signal to noise ratio
Fusion reactions
Error probability
Monitoring
Sensors

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Wang, Z., Wu, J., Yang, J., & Lin, H. (2016). Optimal energy efficient level set estimation of spatially-temporally correlated random fields. In 2016 IEEE International Conference on Communications, ICC 2016 [7511400] (2016 IEEE International Conference on Communications, ICC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2016.7511400
Wang, Zuoen ; Wu, Jingxian ; Yang, Jing ; Lin, Hai. / Optimal energy efficient level set estimation of spatially-temporally correlated random fields. 2016 IEEE International Conference on Communications, ICC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. (2016 IEEE International Conference on Communications, ICC 2016).
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Wang, Z, Wu, J, Yang, J & Lin, H 2016, Optimal energy efficient level set estimation of spatially-temporally correlated random fields. in 2016 IEEE International Conference on Communications, ICC 2016., 7511400, 2016 IEEE International Conference on Communications, ICC 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE International Conference on Communications, ICC 2016, Kuala Lumpur, Malaysia, 5/22/16. https://doi.org/10.1109/ICC.2016.7511400

Optimal energy efficient level set estimation of spatially-temporally correlated random fields. / Wang, Zuoen; Wu, Jingxian; Yang, Jing; Lin, Hai.

2016 IEEE International Conference on Communications, ICC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7511400 (2016 IEEE International Conference on Communications, ICC 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Level set estimation (LSE) is the process of classifying the region(s) that the values of an unknown function exceed a certain threshold. It has a wide range of applications such as spectrum sensing or environment monitoring. In this paper, we study the the optimal LSE of a linear random field that changes with respect to time. A linear sensor network is used to take discrete samples of the spatially-temporally correlated random field in both the space and time domain, and the sensors operate under a total power constraint. The samples are congregated at a fusion center (FC), which performs LSE of the random field by using the noisy observation of the samples. Under the Gaussian process (GP) framework, we first develop an optimal LSE algorithm that can minimize the LSE error probability. The results are then used to derive the exact LSE error probability with the assistance of frequency domain analysis. The analytical LSE error probability is expressed as an explicit function of a number of system parameters, such as the distance between two adjacent nodes, the sampling period in the time domain, the signal-to-noise ratio (SNR), and the spatial-temporal correlation of the random field. With the analytical results, we can identify the optimum node distance and sampling period that can minimize the LSE error probability.

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Wang Z, Wu J, Yang J, Lin H. Optimal energy efficient level set estimation of spatially-temporally correlated random fields. In 2016 IEEE International Conference on Communications, ICC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7511400. (2016 IEEE International Conference on Communications, ICC 2016). https://doi.org/10.1109/ICC.2016.7511400