ADP-based optimal sensor scheduling for target tracking in energy harvesting wireless sensor networks. Issue 6 (August 2016)

• Record Type: Journal Article
• Authors: Song, Ruizhuo; Wei, Qinglai; Xiao, Wendong
• Is Part Of: Neural computing & applications. Volume 27:Issue 6(2016)
• Page Start: 1543
• Page End: 1551
• Publication Date: 2016-08
• Subjects: Adaptive critic designs -- Adaptive dynamic programming -- Approximate dynamic programming -- Neuro-dynamic programming -- Neural networks -- Wireless sensor networks -- Scheduling; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1954-4
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract 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.