Towards Energy Efficient and Robust Cyber-Physical Systems

Sinem Coleri Ergen

Wireless Networks Laboratory, Electrical and Electronics Engineering, Koc University

Cyber-Physical Systems

 System of collaborating computational elements controlling physical entities

Joint Design of Control and Communication Systems

Wireless Networked Control Systems

 Sensors, actuators and controllers connect through a wireless network

Wireless Networked Control Systems

 Benefits of wireless  Ease of installation and maintenance  Low complexity and cost  Large flexibility to accommodate modification and upgrade of components  Backed up by several industrial organizations  International Society of Automation (ISA)  Highway Addressable Remote Transducer (HART)  Wireless Industrial Networking Alliance (WINA)

Trade-off for Communication and Control Systems

 Wireless communication system  Non-zero packet error probability  Unreliability of wireless transmissions  Non-zero delay  Packet transmission and shared wireless medium  Sampling and quantization errors  Signals transmitted via packets  Limited battery resources  Control system  Stringent requirements on timing and reliability  Smaller packet error probability, delay and sampling period  Better control system performance  More energy consumed in wireless communication

Outline

 Optimization of communication system given requirements of control system  Novel design of scheduling algorithms  Joint optimization of control and communication systems  Novel abstractions for control systems

Outline

 Optimization of communication system given requirements of control system  Novel design of scheduling algorithms  Joint optimization of control and communication systems  Novel abstractions for control systems

Novel Scheduling Algorithm Design

 Packet generation period, transmission delay and reliability requirements:  (

T l

,

d l

,

r l

) Network Control Systems  sensor data -> real-time control of mechanical parts  Fixed determinism better than bounded determinism in control systems

Novel Scheduling Algorithm Design

 Adaptivity requirement  Nodes should be scheduled as uniformly as possible

EDF Uniform

Novel Scheduling Algorithm Design

 Adaptivity requirement  Nodes should be scheduled as uniformly as possible 1

EDF Uniform

Novel Scheduling Algorithm Design

 Adaptivity requirement  Nodes should be scheduled as uniformly as possible 2

EDF Uniform

Novel Scheduling Algorithm Design

 Adaptivity requirement  Nodes should be scheduled as uniformly as possible 3

EDF Uniform

Medium Access Control Layer: System Model

     (

T l

,

d l

,

r l

)

T

1 £

T

2 £ given for each link l ...

£

T L T i

/

T

1 =

s i

º max active time=0.9ms

T

1

s i

EDF

max active time=0.6ms ✓

Example Optimization Problem Formulation

Maximum active time of subframes Periodic packet generation Delay requirement Energy requirement Maximum allowed power by UWB regulations Transmission time Transmission rate of UWB for no concurrent transmission case

Outline

 Optimization of communication system given requirements of control system  Novel design of scheduling algorithms  Joint optimization of control and communication systems  Novel abstractions for control systems

Abstractions of Control System

 Maximum Allowable Transfer Interval (MATI): maximum allowed time interval between subsequent state vector reports from the sensor nodes to the controller  Maximum Allowable Delay (MAD): maximum allowed packet delay for the transmission from the sensor node to the controller

MAD MATI

Hard real-time guarantee not possible for wireless -> Packet error probability >0 at any point in time

Abstractions of Control System

 Stochastic MATI: keep time interval between subsequent state vector reports above MATI with a predefined probability to guarantee the stability of control systems  Many control applications and standards already use it  Industrial automation  IEEE 802.15.4e

 Air transportation systems  Cooperative vehicular safety  Never been used in the joint optimization of control and communication systems

Example Optimization Problem Formulation

Total energy consumption Schedulability constraint Stochastic MATI constraint MAD constraint Maximum transmit power constraint

Publications

 Y. Sadi, S. C. Ergen and P. Park, "

Minimum Energy Data Transmission for Wireless Networked Control Systems

", IEEE Transactions on Wireless Communications, vol. 13, no. 4, pp. 2163-2175, April 2014. [ pdf | link ]  [ Y. Sadi and S. C. Ergen, “

Optimal Power Control, Rate Adaptation and Scheduling for UWB-Based Intra-Vehicular Wireless Sensor Networks

”, IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 219-234, January 2013.

pdf | link ]  Y. Sadi and S. C. Ergen, "

Energy and Delay Constrained Maximum Adaptive Schedule for Wireless Networked Control Systems

", submitted.

Projects at WNL

 Intra-Vehicular Wireless Sensor Networks  Supported by Marie Curie Reintegration Grant  Energy Efficient Robust Communication Network Design for Wireless Networked Control Systems  Supported by TUBITAK (The Scientific and Technological Research Council of Turkey)  Energy Efficient Machine-to-Machine Communications  Supported by Turk Telekom  Cross-layer Epidemic Protocols for Inter-vehicular Communication Networks  Supported by Turk Telekom

Thank You!

Sinem Coleri Ergen: [email protected]

Personal webpage: http://home.ku.edu.tr/~sergen Wireless Networks Laboratory: http://wnl.ku.edu.tr