Design of Environmental Sensor Networks Using Evolutionary Algorithms

被引：8
|
作者：
Susanto, Ferry [1 ,2 ]
Budi, Setia [2 ,3 ]
de Souza, Paulo, Jr. [2 ]
Engelke, Ulrich [2 ]
He, Jing [1 ]
机构：
[1] Victoria Univ, Coll Engn & Sci, Footscray, Vic 3011, Australia
[2] Commonwealth Sci & Ind Res Org, Data61, Sandy Bay, Tas 7005, Australia
[3] Univ Tasmania, Sch Engn & Informat & Commun Technol, Sandy Bay, Tas 7005, Australia
来源：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2016年 / 13卷 / 04期
关键词：
Evolutionary algorithm (EA); inverse distance weighting (IDW); leave-one-out cross-validation (LOOCV); multiobjective; optimization; ordinary Kriging (OK); sensor network (SN) deployment; spatial data interpolation; spatial sampling; thin plate spline (TPS); OPTIMIZATION; CHINA;
D O I：
10.1109/LGRS.2016.2525980
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
An evolutionary algorithm (EA)-assisted spatial sampling methodology is proposed to assist decision makers in sensor network (SN) deployments. We incorporated an interpolation technique with leave-one-out cross-validation (LOOCV) to assess the representativeness of a particular SN design. For the validation of our method, we utilized Tasmania's South Esk Hydrological Model developed by the Commonwealth Scientific and Industrial Research Organisation, which includes a range of environmental variables describing the landscape. We demonstrated that our proposed methodology is capable of assisting in the initial design of SN deployment. Ordinary Kriging is shown to be the best suited spatial interpolation algorithm for the EA's LOOCV under the current empirical study.
引用
收藏
页码：575 / 579
页数：5
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