Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks
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 g...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-11-01
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| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2224-2708/1/3/299 |
| _version_ | 1819057855050809344 |
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| author | Alastair R. Allen Daniel N. Nkwogu |
| author_facet | Alastair R. Allen Daniel N. Nkwogu |
| author_sort | Alastair R. Allen |
| collection | DOAJ |
| description | 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 this work is the use of typical obtainable delay series for training the neural network. Most studies have used random generated numbers, which are not a correct representation of delays actually experienced in a wireless network. Our results here shows that adequate prediction of the time delay series using the observer based variable sampling is able to compensate for delays in the communication loop and influences the performance of the control system model. |
| first_indexed | 2024-12-21T13:45:56Z |
| format | Article |
| id | doaj.art-f7ef48fce8254d8c94eaf7c75db9a19f |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-12-21T13:45:56Z |
| publishDate | 2012-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-f7ef48fce8254d8c94eaf7c75db9a19f2022-12-21T19:01:52ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082012-11-011329932010.3390/jsan1030299Adaptive Sampling for WSAN Control Applications Using Artificial Neural NetworksAlastair R. AllenDaniel N. NkwoguWireless 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 this work is the use of typical obtainable delay series for training the neural network. Most studies have used random generated numbers, which are not a correct representation of delays actually experienced in a wireless network. Our results here shows that adequate prediction of the time delay series using the observer based variable sampling is able to compensate for delays in the communication loop and influences the performance of the control system model.http://www.mdpi.com/2224-2708/1/3/299WSANZigbeeANNQoSWPAN |
| spellingShingle | Alastair R. Allen Daniel N. Nkwogu Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks Journal of Sensor and Actuator Networks WSAN Zigbee ANN QoS WPAN |
| title | Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks |
| title_full | Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks |
| title_fullStr | Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks |
| title_full_unstemmed | Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks |
| title_short | Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks |
| title_sort | adaptive sampling for wsan control applications using artificial neural networks |
| topic | WSAN Zigbee ANN QoS WPAN |
| url | http://www.mdpi.com/2224-2708/1/3/299 |
| work_keys_str_mv | AT alastairrallen adaptivesamplingforwsancontrolapplicationsusingartificialneuralnetworks AT danielnnkwogu adaptivesamplingforwsancontrolapplicationsusingartificialneuralnetworks |