Download - Cyber-Physical Systems
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 delayPacket transmission and shared wireless medium
Sampling and quantization errorsSignals 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: Network Control Systems
sensor data -> real-time control of mechanical parts Fixed determinism better than bounded determinism in control systems
(Tl ,dl ,rl )
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
EDF Uniform
1
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
given for each link l Choose subframe length as for uniform allocation Assume is an integer: Allocate every subframes
Uniform distribution minimize max subframe active time
(Tl ,dl ,rl )
T1 ≤ T2 ≤ ...≤ TL
Ti /T1 = si
T1
si
≡EDF
Uniform
max active time=0.9ms
max active time=0.6ms
✓
Example Optimization Problem Formulation
Transmission rate of UWB for no concurrent transmission case
Transmission time
Maximum allowed power by UWB regulations
Energy requirement
Delay requirement
Periodic packet generation
Maximum active time of subframes
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 MATIHard 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
