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
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: (Tl ,dl ,rl )
 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
Medium Access Control Layer: System Model
(Tl ,dl ,rl ) given for each link l
 T1  T2  ... TL

 Choose subframe length as T1 for uniform allocation
 Assume Ti /T1  si is an integer: Allocate every si subframes

 Uniform distribution
minimize max subframe active time

max active time=0.9ms


EDF

Uniform
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
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: sergen@ku.edu.tr
Personal webpage: http://home.ku.edu.tr/~sergen
Wireless Networks Laboratory: http://wnl.ku.edu.tr