1. Integration Science Of Networked System Introduction ARO Site Visit Yuan Xue EECS/ISIS Vanderbilt University

2. General Information Funding From –Army Research Office (W911NF-10-1-0005) –Lockheed Martin (Cost-share) Funding Period: Oct 15, 2009 – Oct 14, 2012 People –Faculties Yuan Xue Xenofon Koutsoukos –Graduate Students Jonathan Wellons Jia Bai Emeka Eyisi Jiannian Weng Wei Yan –Alumni Derek Riley (Assistant Professor at Middle Tennessee State University)

3. Integration Science Of Networked System Project Overview Yuan Xue EECS/ISIS Vanderbilt University

4. Introduction Motivation: Networking systems  larger in scale; more complex in design and function; deeper integration with physical world CPS has extraordinary significance for the future of the U.S. industry and military superiority. Application Domains –Health-Care –Automotive Systems –Building and Process Controls –Defense and Aviation Systems –Critical Infrastructure Cyber-physical systems (CPS) are tight integrations of communications, computational and physical processes

5. Introduction System integration is essential to the large-scale networking system design and development. –Building systems from components is in central to manage the complexity of large-scale networking systems. The objective of this project –Science and technology foundation for the system integration of networking systems –Two aspects: decomposition and interaction

6. Our Research Focuses Focused Area –Mathematical foundations of networking system integration. Controlling complexity by managing interactions across multiple design layers. –Simulation, emulation tools and environments Simulation and emulation environment in support of evaluation, continuous and incremental integration of networking system. Focused System  Network Control System –Two essential components networked controller (multi-hop) wireless networks

7. Theoretical Foundations -Existing Work –Off-the-shelf commercial system: physical and cyber systems are designed and implemented independently based on their own design objectives Their interface is fixed. Limited information exchange between physical and cyber systems leads to sub-optimal performance/system instability. –Existing research: based on a tightly-coupled CPS model with simplified network model Interface between cyber and physical system is unclear for system implementation, with assumption of unlimited information exchange channel between two systems. Unrealistic network model does not consider effect of network routing, queuing, wireless channel contention and scheduling. Plantcontroller socket network Application data Plantcontroller Communication Channel Source coding/channel coding

8. Theoretical Foundations -Our Approach Our Research Approach –formalize the configuration and design as a decomposable, mathematical optimization problem –explicit model the assumptions made between controller/network systems –decompose the design concerns: stability, performance –Manage the complexity of interactions between controller/network systems Two directions of information communication –From controller to network Controller: provides information on performance, information (entropy) of each packet, etc. Network: provides support for (optimal) scheduling, queuing, (re)transmission for controller’s traffic –From network to controller Network: provides load/congestion/loss information Controller: provides support for sampling rate adaptation, packet encoding, etc Decompose the design concerns –Ensure stability of the control system: passivity design –Ensure the robustness of the wireless networks Plantcontroller network Application Data/ performance/ entropy Load Congestion Loss

9. Theoretical Foundations -Results Direction 1: From controller to network –Essential information from controller to network Performance –Network support Optimal retransmission scheme at MAC layer Future directions: –Essential information from controller to network Packet information entropy –Network support Information entropy-aware Queue management/scheduling

10. Theoretical Foundations -Results Direction 2: From network to controller –Essential information from network to controller Congestion signal (in terms of price) –controller support Sampling rate adaptation Future Directions –Controller state awareness –Consider contention loss, random loss,

11. Theoretical Foundations -Results Passivity-based design of networked control systems –Decouple stability from network implementation side effects Robust Wireless Network Resource Management –Ensure robust wireless network performance under uncertain traffic demands –Enhance wireless network performance with knowledge of traffic demands –Future Direction I: consider uncertain/fluctuating channel conditions –Future Direction II: automatic recovery/adaptation for resilient wireless network resource management

12. Tools and Experiment Environments Two major platforms –Simulation platform Network Control System Wind Tunnel (NCSWT) NS2 + Matlab/Simulink –Emulation platform DETERlab + Matlab/Simulink

13. NCSWT System ArchitectureRunning Experiment View Experiment Output Matlab outputNS2 Trace plot

14. Emulation Platform Run-Time Infrastructure (RTI) Model Integration Layer Experiment Specification Network Models Controller Models Organization Models Environment Models Fusion Models Emulation Federate Simulink Federate CPN Federate Delta3D Federate DEVS Federate Simulation Platform Simulation- Emulation Tunnel Model Run-time Network Applications Emulation Platform DETERlab Data communication Layer (TCP/IP)

15. Topology Model Network Interaction Model Modeling Environment Model Interpreter Run-Time Environment TCL script Configuration/Control Environment Host Assignment Deterlab Emulation Environment C2WT Simulation Environment … Tap C lient EmuGateway Federate RTI Simulink Federate Tap C lient Tap Serve r Federates Involving network communication … Network File System Network Application Code Deployment Model

16. Deployment MetaModel

17. Deployment Model Example

18. Network Interaction MetaModel

19. SendCommandToNetwork NodeName: TBD (ControalStation) ProcName: SendCommand Timestamp:TBD Parameter: Command (String) RecvCommandFromNetwork NodeName: TBD (UAV1) ProcName: RecvCommand Timestamp: TBD PeerNodeName: TBD (ControlStation) PeerProcPort: TBD Parameter: Command (String) SendImageToNetwork NodeName: TBD (UAV1) ProcName: SendImage Timestamp: TBD Parameter: ImageURL (String): RecvImageFromNetwork NodeName: TBD (ControlStation) ProcName: RecvImage Timestamp: TBD PeerNodeName: TBD (UAV 1) PeerProcPort:TBD Parameter: PacketDelay(double)

20. UAV federate ControlStatio n federate EmuGateway federate Tap C lient Tap Server HostMap NodeName: HostIP SendImageRecvImage UDP Emulation Host for UAV1 Interaction Delivery Protocol Emulation Host for ControlStation RecvCommandSendCommand TCP Task buffer Task buffer LocalTask buffer Interaction Handler Emulation Env Simulation Env Time converter RTI Time converter HostMap Run-time Experiment View

21. Education and Outreach “Evaluating the Impact of Security Attacks on Cyber-Physical Systems,” Research Experiences for Undergraduates in Cyber Security and Trustworthy Systems (TRUST-REU). “Assessing Security of Cyber-Physical Systems, ” Yuan Xue and Jia Bai, Women’s Institute in Summer Enrichment Sponsored by the Team for Research in Ubiquitous Secure Technology (TRUST)Women’s Institute in Summer Enrichment Sponsored by the Team for Research in Ubiquitous Secure Technology (TRUST)