ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks

被引：15
|
作者：
Song, Ruizhuo [1 ]
Wei, Qinglai [2 ]
Xiao, Wendong [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2016年 / 27卷 / 06期
基金：
北京市自然科学基金; 中国国家自然科学基金; 中国博士后科学基金;
关键词：
Adaptive critic designs; Adaptive dynamic programming; Approximate dynamic programming; Neuro-dynamic programming; Neural networks; Wireless sensor networks; Scheduling; TIME NONLINEAR-SYSTEMS; ONLINE LEARNING CONTROL; CONTROL SCHEME;
D O I：
10.1007/s00521-015-1954-4
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a novel sensor scheduling scheme based on adaptive dynamic programming, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks with solar energy harvesting. Neural network is used to model the solar energy harvesting. Kalman filter estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The presented method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach.
引用
收藏
页码：1543 / 1551
页数：9
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