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Title: Quality of service optimization and adaptive learning in wireless sensor actuator networks for control applications
Author: Nkwogu, Daniel Nnaemeka
ISNI:       0000 0004 5365 3451
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2014
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Wireless sensor actuator networks (WSANs) are becoming a solution for the implementation of control applications. Sensors and actuators can be deployed forming a large or dense network to monitor and control physical parameters or systems. However, this comes with challenges. Reliable data transmission and real-time communication constraints are the most significant challenges in WSANs for control applications because wireless networks are characterised by harsh transmission conditions. The use of WSANs for critical control applications has not gained sufficient progress as wireless networks are perceived to be totally unreliable and hence unsuitable. This makes reliable data transmission a priority in this research. Control applications will have a number of quality of service (QoS) requirements, such as requiring a very low packet-loss rate (PLR), minimum delay and guaranteed packet delivery. The overall goal of this research is to develop a framework that ensures reliable and real-time communication within the sensor network. A totally reliable network design involves ensuring reliability in areas such as the medium access control, connectivity, scalability, lifetime, clustering and routing with trade-offs such as energy consumption, system throughput and computational complexity. In this thesis, we introduce a unique method of improving reliability and real-time communication for control applications using a link quality routing mechanism which is tied into the ZigBee addressing scheme. ZigBee routing protocols do not consider link quality when making routing decisions. The results based on common network test conditions give a clear indication of the impact on network performance for various path loss models. The proposed link quality aware routing (LQAR) showed a highly significant 20.5% improvement in network delays against the ZigBee hierarchical tree routing (HTR) protocol. There is also a 17% improvement in the PLR. We also investigate variable sampling to mitigate the effects of delay in WSANs using a neural network delay predictor and observer based control system model. Our focus on variable sampling is to determine the appropriate neural network topology for delay prediction and the impact of additional neural network inputs such as PLR 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. In addition, results show that the use of network packet loss information improves the prediction accuracy of delay. Our results show that adequate prediction of the time-delay series using the observer based variable sampling model influences the performance of the control system model under the assumptions and stated conditions.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Wireless sensor networks ; Automatic control