Real-Time Wireless Control Networks for Cyber-Physical Systems Chenyang Lu Cyber-Physical Systems Laboratory Department of Computer Science and Engineering
Wireless Control Networks Ø Real-time Sensor Ø Reliability Ø Control performance Actuator sensor data control command Controller 2
Wireless for Process Automation Ø World-wide adoption of wireless in process industries 1.5+ billion hours opera6ng experience 100,000s of smart wireless field devices 10,000s of wireless field networks Offshore Onshore Courtesy: Emerson Process Management Killer App of Sensor Networks! 3
WirelessHART Ø Industrial-grade reliability Multi-channel TDMA MAC One transmission per channel Redundant routes Over IEEE 802.15.4 PHY Ø Centralized network manager collects topology information generates routes and transmission schedule changes when devices/links break Industrial wireless standard for process monitoring and control 4
Our Endeavor 1. Real-time scheduling theory for wireless 2. Wireless-control co-design 3. Case study: wireless structural control 5
Real-Time Scheduling for Wireless Goals Ø Real-time transmission scheduling à meet end-to-end deadlines Ø Fast schedulability analysis à online admission control and adaptation Approach Ø Leverage real-time scheduling theory for processors Ø Incorporate uniue wireless characteristics Results Ø Fixed priority scheduling Delay analysis [RTAS 2011, TC, RTSS 2015] Priority assignment [ECRTS 2011] Ø Dynamic priority scheduling Conflict-aware Least Laxity First [RTSS 2010] Delay analysis for Earliest Deadline First [IWQoS 2014] 6
Real-Time Flows Ø Flow: sensor à controller à actuator over mul6-hops highest lowest priority Ø A set of flows F={F 1, F 2,, F N } ordered by priori6es Ø Each flow F i is characterized by A source (sensor), a des6na6on (actuator) A route through the controller A period P i A deadline D i ( P i ) Total number of transmissions C i along the route 7
Scheduling Problem Ø Fixed priority scheduling Every flow has a fixed priority Order transmissions based on the priorities of their flows. Ø Flows are schedulable if delay i D i for every flow F i Ø Goal: efficient delay analysis end-to-end delay of F i deadline of F i Gives an upper bound of the end-to-end delay for each flow Used for online admission control and adaptation 8
End-to-End Delay Analysis Ø A lower priority flow is delayed due to channel contention: all channels in a slot are assigned to higher priority flows transmission conflict involving a same node 3 2 1 4 5 Ø Analyze each type of delay separately 1 and 5 are conflicting 4 and 5 are conflicting 3 and 4 are conflict-free Ø Combine both delays à end-to-end delay bound 9
Insights Ø Flows vs. Tasks Similar: channel contention Different: transmission conflict Ø Channel contention à multiprocessor scheduling A channel à a processor Flow F i à a task with period P i, deadline D i, execution time C i Leverage existing response time analysis for multiprocessors Ø Account for delays due to transmission conflicts 10
Delay due to Conflict Ø Low-priority flow F l and highpriority flow F h, conflict à delay F l )*$%+*, F l delayed by 2 slots Ø Q(I,h): #transmissions of F h sharing nodes with F l In the worst case, F h can delay F l by Q(l,h) slots Q(l,h) = 5 à F h can delay F l by 5 slots F l delayed by 2 slots F l delayed by 1 slot!"#$%&'"(!" # &!"#$%&'"(!" $ 11
WirelessHART Tested Ø Implementation on a testbed of 69 TelosB motes. Ø WirelessHART stack on TinyOS/mote. Ø Network manager (scheduler + routing). M. Sha, D. Guna6laka, C. Wu and C. Lu, Implementa6on and Experimenta6on of Industrial Wireless Sensor- Actuator Network Protocols, European Conference on Wireless Sensor Networks (EWSN), February 2015. 12
Outline Ø WirelessHART: real-time wireless in industry Ø Real-time scheduling theory for wireless Ø Wireless-control co-design Ø Case study: wireless structural control 13
Wireless-Control Co-Design Goal: opamize control performance over wireless Challenge Ø Wireless resource is scarce and dynamic Ø Cannot afford separating wireless and control designs Cyber-Physical Systems Approach Ø Holistic co-design of wireless and control Examples Ø Rate selection for wireless control [RTAS 2012, TECS] Ø Wireless structural control [ICCPS 2013] Ø Wireless process control [ICCPS 2015] 14
Rate Selection for Wireless Control Ø Optimize the sampling rates of control loops sharing a WirelessHART network. Ø Rate selection must balance control and network delay. Low sampling rate à poor control performance High sampling rate à long delay à poor control performance 15
Control Performance Index Ø Digital implementation of control loop i Periodic sampling at rate f i Performance deviates from continuous counterpart Ø Control cost of control loop i under rate f i [Seto RTSS 96] Approximated as α i e β i f i with sensitivity coefficients α i, β i Ø Overall control cost of n loops: n i=1 α i e β i f i 16
The Rate Selection Problem Ø Constrained non-linear optimization Ø Determine sampling rates f = { f 1, f 2,, f n } minimize control cost n i=1 α i e β i f i subject to delay i 1/ f i f i min f i f i max Delay bound 17
Polynomial Time Delay Bounds Ø In terms of decision variables (rates), the delay bounds are Lagrange dual of objec6ve Non-linear Non-convex Non-differentiable Rate of control loop 6 18
Wireless-Control Co-Design Relax delay bound to simplify optimization Ø Derive a convex and smooth, but less precise delay bound. Rate selection becomes a convex optimization problem. Control cost 19
Evaluation Control Cost 30 25 20 15 10 5 Greedy Heuristic Subgradient Convex Approximation Simulated Annealing Execution Time (seconds) 10 6 10 4 10 2 10 0 Greedy Heuristic Subgradient Convex Approximation Simulated Annealing 0 5 10 15 20 25 30 Number of Control Loops 10 2 5 10 15 20 25 30 Number of Control Loops Greedy heuristic is fast but incurs high control cost. Subgradient method is neither efficient nor effective. Simulated annealing incurs lowest control cost, but is slow. Convex approximation balances control cost and execution time. 20
Wireless Structural Control Ø Structural control systems protect civil infrastructure. Ø Wired control systems are costly and fragile. Ø Wireless structural control achieves flexibility and low cost. Heritage tower crumbles down in earthuake of Finale Emilia, Italy, 2012. Hanshin Expressway Bridge ader Kobe earthuake, Japan, 1995. 11/16/15 21
Contributions Ø Wireless Cyber-Physical Simulator (WCPS) Capture dynamics of both physical plants and wireless networks Enable holistic, high-fidelity simulation of wireless control systems Integrate TOSSIM and Simulink/MATLAB Open source: http://wcps.cse.wustl.edu Ø Realistic case studies on wireless structural control Wireless traces from real-world environments Structural models of a building and a large bridge Excited by real earthuake signal traces Ø Cyber-physical co-design End-to-end scheduling + control design Improve control performance under wireless delay and loss B. Li, Z. Sun, K. Mechitov, G. Hackmann, C. Lu, S. Dyke, G. Agha and B. Spencer, Realis6c Case Studies of Wireless Structural Control, ACM/IEEE Interna6onal Conference on Cyber-Physical Systems (ICCPS'13), April 2013. 11/16/15 22
Bill Emerson Memorial Bridge (a) Ø Main span: 1,150 ft. Ø Carries up to 14,000 cars a day over Mississippi. Ø In the New Madrid Seismic Zone Ø Replaced joints of the bridge by actuators 24 hydraulic actuators Ø Vibration mode: 0.1618 Hz for 1st mode 0.2666 Hz for 2nd mode 0.3723 Hz for 3rd mode (b) 11/16/15 23
Jindo Bridge: Wireless Traces Ø Largest wireless bride deployment [Jang 2010] 113 Imote2 units; Peak acceleration sensitivity of 5mg 30mg Ø RSSI/noise traces from 58-node deck-network for this study 11/16/15 24
Reduction in Max Control Power Cyber-physical co-design à 50% reduc6on in control power. 25
Conclusion Ø Real-time wireless is a reality today Industrial standards: WirelessHART, ISA100 Field deployments world wide Ø Real-time scheduling theory for wireless Leverage real-time processor scheduling Incorporate uniue wireless properties Ø Cyber-physical co-design of wireless control systems Rate selection for wireless control systems Scheduling-control co-design for wireless structural control Ø WCPS: Wireless Cyber-Physical Simulator Enable holistic simulations of wireless control systems Realistic case studies of wireless structural control 26
Future Directions Ø Scaling up wireless control networks From 100 nodes à 10,000 nodes Dealing with dynamics locally Hierarchical or decentralized architecture Ø Science and engineering of wireless control Case studies à unified theory, architecture and methodology Bridge the gap between theory and systems Textbook on cyber-physical co-design 27
For More Information Ø C. Lu, A. Saifullah, B. Li, M. Sha, H. Gonzalez, D. Gunatilaka, C. Wu, L. Nie and Y. Chen, Real-Time Wireless Sensor-Actuator Networks for Industrial Cyber-Physical Systems, Special Issue on Industrial Cyber-Physical Systems, Proceedings of the IEEE, accepted. Ø Real-Time Industrial Wireless Sensor-Actuator Networks: http://cps.cse.wustl.edu/index.php/real-time_wireless_control_networks Ø Wireless Structural Health Monitoring and Control: http://bridge.cse.wustl.edu Ø Wireless Cyber-Physical Simulator: http://wcps.cse.wustl.edu 28