WINLAB Rutgers, The State University of New Jersey - PowerPoint PPT Presentation

Cognitive Radio Networks at Cognitive Radio Networks at WINLAB: Networking and WINLAB: Networking and Security Research Security Research WINLAB Rutgers, The State University of New Jersey www.winlab.rutgers.edu Contact: Professor Wade Trappe, Associate Director trappe@winlab.rutgers.edu

Research Where Wireless Research is Heading: Robust, Adaptive and Where Wireless Research is Heading: Robust, Adaptive and Programmable Multi- -Radio Networks Radio Networks Programmable Multi � The community is researching new architectures, protocols and algorithms for robust/secure networks will allow for a 2 nd generation “cognitive radio” MANET – Collaborative PHY for increased resilience at the radio level – Novel MANET architectures and protocols (cross-layer, global control, adaptive) – Innovative approaches to security in MANETs – Cognitive radio technology to enable spectrum agility and adaptation – Protocols for networking of cognitive radios � Research backed up by a comprehensive set of laboratory capabilities for realistic and reproducible evaluations at scale – Cognitive radio platforms (GNU/URSP, WiNC2R, WARP) – ORBIT radio grid testbed, with upgrade to programmable radios – Outdoor vehicular testing – Integration with wired network testbeds, PlanetLab & VINI � Government will be affected by: – Rapidly evolving technologies with high levels of flexibility – More universal connectivity � Is this really a good thing? – New forms of security risks WINLAB [2]

Research WINLAB “ “CogNet CogNet” ” projects use unique hardware capabilities projects use unique hardware capabilities WINLAB and software design and software design � Global Control Plane (GCP) – Common framework for spectrum allocation, PHY/MAC bootstrap, topology discovery, cross-layer routing and security management – Decentralized coordination of protocols across location and layers � Data plane – Dynamically linked spectrum mgmt, PHY, MAC, Network modules and parameters as specified by control plane protocol Logical separation of control & data for flexible design and low overhead � – Minimize contention between control & data (…>>50% overhead in 802.11 networks!) Adaptive Networks of Cognitive Radios Data Plane Global Control Plane to the Internet Data Plane Control Plane Control Signalling Application Data Naming Transport Boot- Disco Path & PHY1/MAC1 strap very Establish Addres Network ment sing MAC PHY3/MAC3 Control MAC PHY Control PHY Radios with Protocol Stack with GCP Programmable PHY/MAC PHY2/MAC2 WiNC2R platform WINLAB [3]

Research Global Control Planes support a Common Spectrum Global Control Planes support a Common Spectrum Coordination Channel (CSCC) for cognitive networks Coordination Channel (CSCC) for cognitive networks � CSCC enables mutual observation between heterogeneous nodes to explicitly coordinate spectrum usage • Exchange of CSCC messages by an extra narrow-band (low bit- rate) radio • Periodically broadcast self-states to others • Coordinate spectrum usage WINLAB [4]

Research CogRadio Nets: Integrating routing and MAC layer functions Nets: Integrating routing and MAC layer functions CogRadio is possible via the Global Control Plane is possible via the Global Control Plane � Global scheduling of routes and MAC time slots is possible through the Global Control Plane architecture � Such scheduling eliminates usual contention that degrades conventional wireless system designs � Allocation algorithm works on both frequency (FD) and time (TD) � Algorithm checks for compatible time slot and freq at each receiver Comparison of Individual and Aggregate � Allows for more parallel transmissions Throughput (fewer “exposed nodes”) and eliminates 1400000 packet contention Global Scheduling 1200000 � Significant performance improvement 802.11 1000000 over conventional layered 802.11 + 800000 Aloha 600000 Slot Aloha AODV etc. 400000 � Requires GCP-type capability for 200000 distribution of control 0 flow 1 flow 2 flow 3 flow 4 flow 5 Total WINLAB [5]

Research Warp- -5: An AI 5: An AI- -based based “ “Wireless Adaptive Routing Protocol Wireless Adaptive Routing Protocol” ” Warp exploits control- -plane separation for improvement plane separation for improvement exploits control � Overview: AODV-based Routing: Overload – Intended for the (CBMANET) CLAN architecture – Investigated by Brian Russel and Michael Littman in conjunction with WINLAB AODV (1 radio) – Based on machine learning algorithms AODV (Control Plane) � Design philosophy: Warp-5 (1 radio) – Routing protocols generally make decisions based on Warp-5 (Control Plane) metrics that don’t reflect quality of service objectives 12000 – Shortest route may not always be the fastest or best! – Cross-layer factors should be considered: 10000 Total Packets Delivered MAC/ PHY level data: SNR, RSS, sym bol � error rate, bit error rate? What about router congestion? 8000 � WARP-5: – Distance vector, on-demand routing protocol for ad WARP-5 Routing: Automated Balancing hoc networks 6000 – Time-based routing metric incorporates router congestion level and environmental 4000 noise/interference. – Routes around heavily-used routers and noisy links even if the route is longer. 2000 – Nodes learn estimated time-to-destination for all neighbors. 0 – Protocol benefits present for both single channel and 1 2 3 4 5 6 control-channel architectures Simulation Trial Runs WINLAB [6]

Research AUSTIN: An Initiative to A Ass ssu ure re S Software Radios have oftware Radios have AUSTIN: An Initiative to Trusted rusted In Interactions teractions T � Goal: to regulate the future radio environment, ensure trustworthy cognitive radio operation (Team: Rutgers, Virginia Tech, UMass) How — two complementary mechanisms � – On-board enforcement – restrict any violation attempt from accessing the radio: � Each CR runs its ow n suite of spectrum etiquette protocols � Onboard policy checking verifies actions occur according to “ spectrum law s ” – An external monitoring infrastructure: � Distributed Spectrum Authority (DSA) — police agent observes the radio environm ent � DSA w ill punish CRs if violations are detected via authenticated kill com m ands. WINLAB [7]

Research AUSTIN involves formalizing security languages for CR AUSTIN involves formalizing security languages for CR regulation and a security management plane regulation and a security management plane � AUSTIN will use law-governed interaction ( LGI ), which is more powerful than conventional access control in both expressive power and scalability. – LGI employs locality , which supports decentralization of access control, and scalability for stateful regulation – LGI can achieve global effects over a community because all members of that community are subject to the same law � A broad and expressive regulatory language will be designed – XGPL is a starting point, but does not involve policy enforcement – AUSTIN-XGPL will use a concrete representation of past LGI-based Interaction behaviors to allow a detailed evaluation for regulation. – AUSTIN-XGPL challenges: Make the language support variable degrees of � interoperability betw een federations of CR devices. Make the language pow erful, yet sim ple enough to � m inim ize the risk of a poorly-w ritten/ buggy law � AUSTIN Credo: Security must be “designed into” all future CR devices (e.g. an FCC-imposed requirement) – All CR devices will have a mandatory trusted computing component that includes a well-architected Security Management Plane (SMP) – RF units immediately partition incoming signals to extract SMP communications and relay these to a trusted module on the CR – AUSTIN-SMP will be driven by associated Security Management Agents (SMA) – Security Message Units (SMUs) will support multiple regulation services via a unified packet format. AUSTIN-SMP Architecture – AUSTIN-SMP provides an exciting approach to more provably secure protocols, as well as improved network manageability WINLAB [8]