Adaptive Sampling for Spatial Prediction in Environmental Monitoring using Wireless Sensor Networks: A Review

被引：0

作者：

Linh Nguyen ^{[1
]}

Ulapane, Nalika ^{[1
]}

Miro, Jaime Valls ^{[1
]}

机构：

[1] Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Autonomous Syst, Sydney, NSW, Australia

来源：

PROCEEDINGS OF THE 2018 13TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2018) | 2018年

关键词：

MARKOV RANDOM-FIELDS; GAUSSIAN-PROCESSES; SELECTION; DESIGN; OPTIMIZATION; ALGORITHMS; APPROXIMATION; PLACEMENT; STRATEGY;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed.

引用

页码：346 / 351

页数：6