Real-Time �Wireless Control Networks�for Cyber-Physical Systems

ChenyangLuCyber-PhysicalSystemsLaboratoryDepartmentofComputerScienceandEngineering

sensordata

Sensor

Actuator

controlcommand

Wireless Control Networks

2

Ø  Real-time

Ø  Reliability

Ø  Control performance

Controller

Wireless for Process AutomationØ World-wide adoption of wireless in process industries

3

1.5+billionhoursopera6ngexperience

100,000sofsmartwirelessfielddevices

10,000sofwirelessfieldnetworks Courtesy:EmersonProcessManagement

Offshore Onshore

KillerAppofSensorNetworks!

WirelessHART

Industrialwirelessstandardforprocessmonitoringandcontrol

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Ø  Industrial-grade reliabilityq  Multi-channel TDMA MAC

q  One transmission per channel

q  Redundant routes

q  Over IEEE 802.15.4 PHY

Ø Centralized network managerq  collects topology informationq  generates routes and

transmission scheduleq  changes when devices/links break

Our Endeavor

1.  Real-time scheduling theory for wireless

2.  Wireless-control co-design

3.  Case study: wireless structural control

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Real-Time Scheduling for WirelessGoalsØ  Real-time transmission scheduling à meet end-to-end deadlinesØ  Fast schedulability analysis à online admission control and adaptation

ApproachØ  Leverage real-time scheduling theory for processorsØ  Incorporate unique wireless characteristics

ResultsØ  Fixed priority scheduling

q  Delay analysis [RTAS 2011, TC, RTSS 2015]q  Priority assignment [ECRTS 2011]

Ø  Dynamic priority schedulingq  Conflict-aware Least Laxity First [RTSS 2010]q  Delay analysis for Earliest Deadline First [IWQoS 2014]

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Real-Time Flows

Ø  Flow:sensoràcontrolleràactuatorovermul6-hops

Ø  AsetofflowsF={F1,F2,…,FN}orderedbypriori6esØ  EachflowFiischaracterizedby

q  Asource(sensor),ades6na6on(actuator)q  Aroutethroughthecontrollerq  AperiodPiq  AdeadlineDi(≤Pi)q  TotalnumberoftransmissionsCialongtheroute

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highest lowest priority

Scheduling Problem

Ø  Fixed priority schedulingq  Every flow has a fixed priority

q  Order transmissions based on the priorities of their flows.

Ø  Flows are schedulable if delayi ≤ Di for every flow Fi

Ø Goal: efficient delay analysis

q  Gives an upper bound of the end-to-end delay for each flow

q  Used for online admission control and adaptation

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end-to-end delay of Fi

deadline of Fi

End-to-End Delay Analysis

Ø A lower priority flow is delayed due toq  channel contention: all channels in a slot are assigned

to higher priority flowsq  transmission conflict involving a same node

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2 1

1 and 5 are conflicting 4 and 5 are conflicting

4 5 3

3 and 4 are conflict-free

Ø Analyzeeachtypeofdelayseparately

Ø Combinebothdelaysàend-to-enddelaybound

InsightsØ  Flows vs. Tasks

q  Similar: channel contention

q  Different: transmission conflict

Ø  Channel contention à multiprocessor schedulingq  A channel à a processor

q  Flow Fi à a task with period Pi, deadline Di, execution time Ci

q  Leverage existing response time analysis for multiprocessors

Ø Account for delays due to transmission conflicts

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&!"#$%&'"(!"$

)*$%+*,

Delay due to Conflict

Ø  Low-priority flow Fl and high-priority flow Fh, conflict à delay Fl

Ø  Q(I,h): #transmissions of Fh sharing nodes with Fl q  In the worst case, Fh can

delay Fl by Q(l,h) slots q  Q(l,h) = 5 à Fh can delay Fl

by 5 slots

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Fl delayed by 2 slots

Fl delayed by 2 slots

Fl delayed by 1 slot

WirelessHART Tested

Ø  Implementation on a testbed of 69 TelosB motes.Ø WirelessHART stack on TinyOS/mote.

Ø Network manager (scheduler + routing).

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M.Sha,D.Guna6laka,C.WuandC.Lu,Implementa6onandExperimenta6onofIndustrialWirelessSensor-ActuatorNetworkProtocols,EuropeanConferenceonWirelessSensorNetworks(EWSN),February2015.

OutlineØ WirelessHART: real-time wireless in industryØ  Real-time scheduling theory for wireless

Ø Wireless-control co-designØ Case study: wireless structural control

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Wireless-Control Co-Design

ChallengeØ  Wireless resource is scarce and dynamic

Ø  Cannot afford separating wireless and control designs

Cyber-Physical Systems ApproachØ  Holistic co-design of wireless and control

ExamplesØ  Rate selection for wireless control [RTAS 2012, TECS]

Ø  Wireless structural control [ICCPS 2013]

Ø  Wireless process control [ICCPS 2015]

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Goal:opAmizecontrolperformanceoverwireless

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.q  Lowsamplingrateàpoorcontrolperformanceq  Highsamplingrateàlongdelayàpoorcontrolperformance

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Ø  Control cost of control loop i under rate fi [Seto RTSS’96] q  Approximated as with sensitivity coefficients

Control Performance Index

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Ø  Digital implementation of control loop iq  Periodic sampling at rate fiq  Performance deviates from continuous counterpart

€

α i e−β i fi

Ø  Overall control cost of n loops:

€

α i e−β i fi

i=1

n

∑€

α i, βi

delayi ≤1/ fi

The Rate Selection Problem

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€

f = { f1, f2,, fn}

€

fimin ≤ f i ≤ f i

max

minimize control cost

€

α i e−β i fi

i=1

n

∑

subject to

Ø  Constrained non-linear optimization

Ø  Determine sampling rates

Delay bound

Polynomial Time Delay Bounds

Ø  In terms of decision variables (rates), the delay bounds are

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q  Non-linear q  Non-convex q  Non-differentiable

Lagrangedualofo

bjec6ve

Rateofcont

rolloop6

Wireless-Control Co-Design

Relax delay bound to simplify optimizationØ Derive a convex and smooth, but less precise delay bound.

➠ Rate selection becomes a convex optimization problem.

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Controlcost

Evaluation

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q  Greedy heuristic is fast but incurs high control cost.q  Subgradient method is neither efficient nor effective.q  Simulated annealing incurs lowest control cost, but is slow.

q  Convex approximation balances control cost and execution time.

5 10 15 20 25 300

5

10

15

20

25

30

Con

trol

Cos

t

Number of Control Loops

Greedy Heuristic Subgradient Convex Approximation Simulated Annealing

5 10 15 20 25 3010−2

100

102

104

106

Exe

cutio

n Ti

me

(sec

onds

)

Number of Control Loops

Greedy Heuristic Subgradient Convex Approximation Simulated Annealing

Wireless Structural ControlØ  Structural control systems protect civil infrastructure.Ø  Wired control systems are costly and fragile. Ø  Wireless structural control achieves flexibility and low cost.

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HeritagetowercrumblesdowninearthquakeofFinaleEmilia,Italy,2012.

HanshinExpresswayBridgeaderKobeearthquake,Japan,1995.

Contributions

Ø  Wireless Cyber-Physical Simulator (WCPS)q  Capture dynamics of both physical plants and wireless networksq  Enable holistic, high-fidelity simulation of wireless control systemsq  Integrate TOSSIM and Simulink/MATLABq  Open source: http://wcps.cse.wustl.edu

Ø  Realistic case studies on wireless structural controlq  Wireless traces from real-world environmentsq  Structural models of a building and a large bridgeq  Excited by real earthquake signal traces

Ø  Cyber-physical co-designq  End-to-end scheduling + control designq  Improve control performance under wireless delay and loss

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B.Li,Z.Sun,K.Mechitov,G.Hackmann,C.Lu,S.Dyke,G.AghaandB.Spencer,Realis6cCaseStudiesofWirelessStructuralControl,ACM/IEEEInterna6onalConferenceonCyber-PhysicalSystems(ICCPS'13),April2013.

Bill Emerson Memorial Bridge

Ø  Main span: 1,150 ft. Ø  Carries up to 14,000 cars

a day over Mississippi. Ø  In the New Madrid

Seismic ZoneØ  Replaced joints of the

bridge by actuatorsq  24 hydraulic actuators

Ø  Vibration mode:q  0.1618 Hz for 1st modeq  0.2666 Hz for 2nd modeq  0.3723 Hz for 3rd mode

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(a)

(b)

Jindo Bridge: Wireless Traces

Ø  Largest wireless bride deployment [Jang 2010] q  113 Imote2 units; Peak acceleration sensitivity of 5mg – 30mg

Ø  RSSI/noise traces from 58-node deck-network for this study

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Reduction in Max Control Power

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Cyber-physicalco-designà50%reduc6onincontrolpower.

Conclusion

Ø  Real-time wireless is a reality todayq  Industrial standards: WirelessHART, ISA100q  Field deployments world wide

Ø  Real-time scheduling theory for wirelessq  Leverage real-time processor schedulingq  Incorporate unique wireless properties

Ø Cyber-physical co-design of wireless control systemsq  Rate selection for wireless control systemsq  Scheduling-control co-design for wireless structural control

Ø WCPS: Wireless Cyber-Physical Simulatorq  Enable holistic simulations of wireless control systemsq  Realistic case studies of wireless structural control

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Future DirectionsØ  Scaling up wireless control networks

q  From 100 nodes à 10,000 nodes

q  Dealing with dynamics locally

q  Hierarchical or decentralized architecture

Ø  Science and engineering of wireless controlq  Case studies à unified theory, architecture and methodology

q  Bridge the gap between theory and systems

q  Textbook on cyber-physical co-design

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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

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