System level simulation of wireless networked control systems Simulation and implementation platform: PiccSIM

1. System level simulation ofwireless networked control systemsSimulation and implementation platform: PiccSIM Lasse Eriksson Shekar Nethi (UV), Mikael Pohjola (TKK/CEL) and Prof. Riku Jäntti (TKK/COM)

2. Outline • Introduction and motivation • Wireless automation • Design tools • PiccSIM-platform • Case studies • Demo

3. Introduction • Old story short: • More computing power available • Smaller and smaller devices (MEMS/NEMS) • Networking capabilities, wireless technology => ubiquitous computing systems

4. Introduction... • Networked control systems are real-time computing and control systems • Sensors, actuators and controllers communicate over a shared medium • Field buses frequently used in control applications

5. Wireless automation • Wireless technology has already changed the consumer markets (cell phones, PDAs, laptops...) • Wireless automation: Embedded and networked control systems where the different devices (sensors, controllers and actuators) communicate seamlessly using wireless technology • Wireless vision: autonomic communications and computing gets rid of the human-in-the-loop by making the systems self-configuring, self-healing, self-optimizing and self-protecting

6. Wireless automation... • Opportunities and challenges? • Connection of field devices through a field bus requires a lot of network planning, wiring and troubleshooting as a result, for many automation systems the cost is “all in the wires” => wireless provides flexibility, reconfigurability, better capabilities for fault diagnostics, and savings in wiring • There are issues regarding e.g. security (out of the scope of this study) • The special characteristics of wireless networking need to be addressed in control design! => varying time-delays, packet losses etc.

7. Towards reliable wireless automation Quality of service Increase robustness Decrease jitter Requirement for control Data fusion PID Controller tuning New control algorithms Increase jitter margin and tolerance to errors Wireless automation systems Coexistence protocols Multi-path routing (mesh) Synchronization Performance of Wireless networks

8. WISA-project (2006-2007) • Wireless sensor and actuator networks for measurement and control • University of Vaasa (prof. Riku Jäntti, Communications group) • Helsinki University of Technology (prof. Heikki Koivo, Control Engineering Laboratory) • Royal Institute of Technology (prof. Mikael Johansson, Automatic Control group and prof. Jens Zander, Radio Communication Systems group) • Funded by TEKES and Vinnova (Nordite-program)

9. Some architectures

10. More architectures

11. Tools for design? • There is a lack of design tools that are able to deal with integrated communication and control systems • TrueTime (Lund University): Network simulation with MATLAB/Simulink • Accuracy of network simulation? • Few network protocols available • Good for control performance analysis (Jitterbug)

12. We need to find a common testing platform for Communication and Control Design Option 1: Develop a New Simulator (example: Java or MATLAB based simulators) Option 2: Integrate existing available simulators Control Design: - MATLAB/Simulink/xPC Target (automatic code generation), MoCoNet-platform Communications Systems: - Ns2, OPNET, QUALNET, SENSE, etc. PiccSIM PiccSIM = MoCoNet + Ns2

13. PiccSIM • Platform for integrated communications and control design, simulation, implementation and modeling = PiccSIM • The key features of the platform are • Support for powerful control design and implementation tools provided by MATLAB enabling automatic code generation from Simulink models for real-time execution • real-time control of a true or simulated process over a user-specified network • capability to emulate any wired/wireless networks readily available in Ns2 • easy-to-use network configuration tool • the platform is accessible over the Internet

14. PiccSIM = MoCoNet + Ns2 The system consists of three computers • Webserver, Database, xPC Host: The server computer is responsible for maintaining connections between users and processes, running a reservation system for controlling processes. • RTOS xPC Target: The computer controls the real process or simulates a process in real-time. Equipped with an I/O controller board. • Network simulator (Ns-2) • Router: All computers are connected through a network router S. Nethi, M. Pohjola, L. Eriksson, R. Jäntti. Platform for Emulating Networked Control Systems in Laboratory Environments, to appear in Proc. IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM 2007), Helsinki, Finland, June 18-21, 2007.

15. An example • Two computers (xPC Target and Ns2) • UDP packets are generated from the signal measured from the process. • Packets are sent on to the network • Ns2 computer using TAP agent captures packets and then node mapping is done using UDP port numbers • On successful reception the packet is sent back to xPC Target

16. Network configuration tool

17. Network configuration

18. Network layout

19. TCL script generation Ns-2

20. Simulation case studies • Building Automation • Factory floor • Target tracking and control Performance comparison of AODV (Single path) and LMNR (Multipath Routing protocol) in different scenarios of industrial wireless systems S. Nethi, M. Pohjola, L. Eriksson, R. Jäntti. Simulation case studies of wireless networked control systems, submitted to the 10th ACM/IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MsWIM’2007), Crete Islands, Greece, October 22-26, 2007

21. LMNR (Localized Multiple next hop routing) Set up multiple routes Next hop is locally decided based on load, interference, and link availability => Increase robustness against link faults (decrease the need for rerouting in case of failures) Multi-path routing AODV AOMDV LMNR S. Nethi, C. Gao and  R Jäntti, “Localized Multiple Next-hop Routing Protocol”, to appear in Proc. 7th international conference on ITS telecommunication (ITST 2007), Paris, France, June 5-8, 2007

22. Building Automation • Physical Models: • Heat balance in rooms (PID control) • CO2 concentration in rooms (relay control) • Event driven signals, lighting (on/off) • Communication Model: • Zigbee motes (15m range) • Ricean propagation channel

23. Results (LMNR vs. AODV) Packet Delivery ratio (%) Results clearly indicate that multipath routing has contributed to increased packet delivery ratio and decreased jitter (delay variance) Avg. end-to-end delay and jitter (sec) • To improve system performance: • Utilize group coordination and data aggregation to localize computation and decrease network traffic • - Redesign of network, i.e. adding more access points

24. Factory floor • Wireless measurement system used for monitoring a complex industrial process (on the top of the control system) • Optimization (coordination) of process performed • Time based (control) and event based (alarms) traffic

25. Factory floor... • Demanding environment for wireless communication • The frequency and time dependent shadow fading caused by the environment can cause certain frequency bands to become unusable, leading to link failure • Moving objects in the environment change the channels’ quality • The interesting metric to investigate is the QoS (i.e. delay bounds and packet loss) of the wireless monitoring system

26. Results (LMNR vs. AODV) As the shadowing deviation increases, so does the probability of link failure. LMNR shows robustness against link failures. Packet Delivery ratio and Normalised routing overhead (%) Delayed information may be outdated: Significant improvement in average end-to-end delay for LMNR over AODV. Avg. end-to-end delay (sec)

27. Target Tracking and Path Management • Two Communication pairs: - Sensors-Controller - Controller-Mobile Node • Propagation model: - Two ray ground model • Results produced for 9 different reference paths Packet delivery fraction and Avg. end-to-end delay Outage time and Error estimate

28. Recorded simulation for Target tracking

29. Questions and Answers? Contact information: Lasse Eriksson Helsinki University of Technology Control Engineering Laboratory P.O.Box 5500 FI-02015 TKK Finland Tel. +358 50 384 1715 Email: lasse.eriksson@tkk.fi