The recent adoption of Wireless Sensor-Actuator Networks (WSANs) in industrial control systems makes it possible to build and maintain infrastructure at low cost. Unlike conventional wireless networks, WSANs have strict constraints to ensure the control performance and stability of physical systems. The main goal of the WSANs research is to maximize the control performance of the WSANs considering the stringent requirements of the control system and external disturbances caused by the harsh industrial environments. In this paper, we propose a control-aware adaptive routing for industrial WSANs. The proposed routing scheme aims to improve the performance of the control system in the conditions that the packet delivery ratio (PDR) of routing paths is not constant due to the unpredictable external interference of WSANs. The criticality of control packet is decided by importance of control commands in consideration of real-time control performances. The control command packet generated by the controller has a different purpose depending on the criticality. High criticality packets are transmitted over the optimal path with the highest PDR measured among the multiple paths. Low criticality packets not only convey control commands to the actuator but are also used to measure the PDR of each path. In addition, an algorithm to detect changes in PDR keeps the PDR of each path up to date. It is possible to improve the performance of the control system by maintaining the maximum probability that the high criticality packets are delivered successfully in a given network situation. The simulation results demonstrate the performance of the proposed routing algorithm.
Table Of Contents
Ⅰ. INTRODUCTION 1 Ⅱ. BACKGROUND 3 2.1 Wireless Sensor-Actuator Networks 3 2.2 Packet Loss Compensation 4 Ⅲ. RELATED WORK 5 Ⅳ. PROPOSED METHOD 6 4.1 Criticality Determination 6 4.2 Low Criticality Packet Routing 7 4.2.1 Round Robin 7 4.2.2 Least Selected Path 8 4.2.3 Oldest Selected Path 9 4.3 High Criticality Packet Routing 9 4.3.1 Largest Empirical Mean 9 4.3.2 Largest Upper-Confidence-Bound 10 4.3.3 LUCB first, LEM later 11 4.4 Detecting Changes in Packet Delivery Ratio 12 4.4.1 Out of the Hoeffding’s Confidence Interval 12 4.4.2 Out of the 3-Sigma Control Limits 13 4.4.3 Out of the 3-Sigma Control Limits with Comparing the likelihood 14 4.5 Responding to Changes in Packet Delivery Ratio 15 Ⅴ. SIMULATION RESULT 16 5.1 Wireless Sensor-Actuator Network Simulation 16 5.1.1 Physical System Model 16 5.1.2 Wireless Network Model 17 5.2 Criticality Determination 18 5.3 Low Criticality Packets Routing 19 5.4 High Criticality Packets Routing 20 5.5 Detecting Changes in Packet Delivery Ratio 23 5.6 The Optimal Routing Path Selection Algorithm 25 Ⅵ. CONCLUSION 27 REFERENCES 28 SUMMARY (Korean) 31