adaptive sampling for wsan control applications using artificial neural networks自适应采样wsan使用人工神经网络控制应用程序.pdfVIP

J. Sens. Actuator Netw. 2012, 1, 299-320; doi:10.3390/jsan1030299 OPEN ACCESS Journal of Sensor and Actuator Networks ISSN 2224-2708 /journal/jsan/ Article Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks Daniel N. Nkwogu and Alastair R. Allen * School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK; E-Mail: r01dnn8@abdn.ac.uk * Author to whom correspondence should be addressed; E-Mail: a.allen@abdn.ac.uk; Tel.: +44-1224-272-501; Fax: +44-1224-272-497. Received: 28 September 2012; in revised form: 29 October 2012 / Accepted: 11 November 2012 / Published: 30 November 2012 Abstract: Wireless sensor actuator networks are becoming a solution for control applications. Reliable data transmission and real time constraints are the most significant challenges. Control applications will have some Quality of Service (QoS) requirements from the sensor network, such as minimum delay and guaranteed delivery of packets. We investigate variable sampling method to mitigate the effects of time delays in wireless networked control systems using an observer based control system model. Our focus for variable sampling methodology is to determine the appropriate neural network topology for delay prediction and also investigate the impact of additional inputs to the neural network such as network packet loss rate and throughput. The major contribution of t