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Real-Time Control Over Wireless Networks

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REAL-TIME CONTROL OVER WIRELESS NETWORKS by VENKATA PRASHANT MODEKURTHY DISSERTATION Submitted to the Graduate School of Wayne State University, Detroit, Michigan in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY 2020 MAJOR: COMPUTER SCIENCE Approved By: Advisor Date arXiv:2011.03169v1 [cs.NI] 6 Nov 2020 DEDICATION To Venkata Sita Rama Murthy Modekurthy, Vijaya Kumari Modekurthy, Venkata Pavan Kumar Modekurthy, and Kalianne Kinsey Modekurthy. ii ACKNOWLEDGEMENTS I would like to sincerely thank my advisor, Dr. Abusayeed Saifullah, for his guidance, cooper- ation, ideas and most-importantly time. I would not be graduating if not for his advice over the last 5 years. I extend my thanks to my co-advisor Dr. Sanjay Madria for his guidance and motivation. I would like to thank Dr. Nathan Fisher, Dr. Zhishan Guo, and Dr. Marco Brocanelli with whom I had wonderful opportunities for collaboration. These collaborations have made my Ph.D. experience productive and inspiring. I would also like to thank my lab mates from the CRI lab at Wayne State University and then Advanced Networking Lab at Missouri S&T. My hearty gratitude for Dr. Corey Tessler and Dr. Amartya Sen for both the personal and professional time which lead to stimulating discussions and collaborations. I am greatly indebted to the the faculty members and staff at both Wayne State for their helping hands in every step of my Ph.D. and notably for making the transfer from Missouri S&T effortless. Most importantly, I would like to thank my parents and wife for their perpetual support and love. I acknowledge the financial support from Dr. Abusayeed Saifullah and Dr. Sanjay Madria in the form of research assistantship through NSF grants, and from Department of Computer Science at Wayne State in the form of research assistantship from internal grants and teaching assistantship. Last but not least, I thank all committee members for their time, suggestions, and service. iii TABLE OF CONTENTS Dedication . ii Acknowledgements . iii List of Tables . x List of Figures . xi Chapter 1 Introduction . 1 Chapter 2 Distributed Graph Routing for WirelessHART Networks . 4 2.1 Introduction . 5 2.2 Related Work . 8 2.3 Network Model . 9 2.4 An Overview of Graph Routing . 11 2.5 Distributed Graph Routing . 12 2.5.1 Protocol Description . 14 2.5.2 Example . 17 2.6 Theoretical Analysis . 19 2.6.1 Convergence and Sub-optimality . 19 2.6.2 Performance Analysis . 20 2.7 Evaluation . 23 2.7.1 Experiment . 23 iv 2.7.2 Simulation Setup . 25 2.7.3 Simulation Results . 26 2.8 Summary . 36 Chapter 3 A Distributed Real-Time Scheduling System for Industrial Wireless Networks . 38 3.1 Introduction . 39 3.2 Related Work . 42 3.3 Background and System Model . 44 3.4 The Design of DistributedHART . 47 3.4.1 Distributed Scheduling . 48 3.4.2 Scheduling Policy . 55 3.4.3 Online Scheduling as Dedicated and Shared Slots . 56 3.5 End-to-end Delay Analysis for DistributedHART . 60 3.6 Non-Uniform Time Window Assignment in DistributedHART . 67 3.6.1 Algorithm to Select Non-Uniform Time Window Length . 67 3.6.2 Non-Uniform Time Window Allocation: . 68 3.6.3 End-to-End Delay Analysis for Non-Uniform Time Window Assignment . 69 3.6.4 Performance Evaluation of Non-Uniform Time Window Assignment . 70 3.7 Latency under DistributedHART . 71 3.8 Testbed Experiments . 75 3.8.1 Evaluation Metrics . 75 3.8.2 Results . 76 v 3.9 Simulation . 79 3.9.1 Simulation Setup . 79 3.9.2 Performance under Varying Window Lengths . 80 3.9.3 Performance under Varying Number of Control Loops considering Har- monic Periods . 81 3.9.4 Performance under Varying Number of Control Loops considering Non- Harmonic Periods . 83 3.9.5 Performance under Varying Number of Nodes . 85 3.9.6 Performance under Varying Hyper-Periods . 87 3.9.7 Performance under Varying Workload Dynamics . 88 3.9.8 Performance of End-to-End Delay Analysis . 90 3.9.9 Comparison in terms of Percentage of Idle Slots . 91 3.9.10 Performance Comparison between DistributedHART and Centralized Sched- uler with Compact Schedules . 93 3.10 Summary . 94 Chapter 4 Low-Latency In-Band Integration of Multiple Low-Power Wide-Area Networks 95 4.1 Introduction . 96 4.2 The SNOW Architecture . 100 4.3 Integrating Multiple SNOWs: An Overview of the Proposed Approach . 101 4.3.1 Related Work and New Challenges . 104 4.3.2 Formulation . 104 4.4 Description of the Low-Latency Integration of Multiple SNOWs . 108 vi 4.4.1 Deriving an Expression of Latency . 109 4.4.2 Deriving a Latency Expression for a special case . 120 4.4.3 Latency and Traffic aware Spectrum Allocation for SNOW Integration (LT- SASI) . 121 4.5 Experiment . 123 4.6 Simulation . 125 4.6.1 Maximum and Average Latencies for Each SNOW . 126 4.6.2 Performance of LT-SASI under Scalability of Number of Nodes . 127 4.6.3 Performance of LT-SASI under Scalability of Number of Base Station . 128 4.7 Summary . 129 Chapter 5 A Utilization-Based Approach for Schedulability Analysis in Wireless Control Systems . 131 5.1 Introduction . 132 5.2 Background . 134 5.3 Related Work . 135 5.4 System Model . 136 5.5 Problem Formulation . 138 5.6 Utilization-Based Schedulability Analysis . 139 5.6.1 Establishing a Utilization Bound Analysis . 139 5.6.2 Transmission Conflict Delay Computation . 143 5.7 Extending the Utilization Bound Analysis to Graph Routing and Hierarchical Net- working . 146 vii 5.7.1 Transmission Conflict Delay Computation for Graph Routing Algorithms . 147 5.7.2 Adopting the Utilization Based Analysis through Hierarchical Networking . 151 5.8 Evaluation . 153 5.8.1 Simulation Setup . 153 5.8.2 Results . 154 5.9 Summary . 157 Chapter 6 Online Period Selection for Wireless Control Systems . 158 6.1 Introduction . 159 6.2 Related Work . 162 6.3 System Model . 163 6.3.1 Network Model . 163 6.3.2 Control System Model . 164 6.4 Link Quality Based Autonomous Scheduler . 166 6.4.1 Link Quality Prediction . 167 6.4.2 Game Formulation for Autonomous Scheduler . 169 6.4.3 Single Time Slot Game . 171 6.4.4 Multiple Time Slot Game . 173 6.4.5 Schedulability Analysis . 174 6.5 Online Sampling Period Selection . 175 6.5.1 Formulation of a Centralized Online Sampling Period Selection Problem . 175 6.5.2 Sampling Period Acclimation . 176 viii 6.5.3 Initial Sampling Period Assignment . 178 6.5.4 State Estimation and Correction . 179 6.5.5 Segregating the Flows . ..
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