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RIDE.Robust.Internetworking in.Disrruupptive.Environments

This project focuses on the development of robust internetworking solutions for disruptive environments. It explores software architectures, secure multicast protocol analysis, formal interoperability, reflection and meta-programming, Petri nets, formal modeling and analysis, automated reasoning tools, policy languages, secure sensor networks, resource management and scheduling, fault-tolerant internetworking, and communications and signal processing.

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RIDE.Robust.Internetworking in.Disrruupptive.Environments

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  1. RIDE Robust Internetworking in Disrruupptive Environments Linda Briesemeister, José Joaquin Garcia-Lunes-Aceves, Hamid R. Sadjadpour, José Meseguer, Mark-Oliver Stehr, Carolyn Talcott DARPA PI Meeting August 2, 2005 Washington DC

  2. RIDE Team University of Illinois at Urbana-Champaign Department of Computer Science José Meseguer (PI) Mark-Oliver Stehr (co-PI) • Software Architectures • Secure, multicast protocol analysis • Formal Interoperability • Reflection and meta-programming • Petri Nets August 1st SRI International Computer Science Laboratory Carolyn Talcott (PI), Mark-Oliver Stehr (co-PI), Linda Briesemeister University of California, Santa Cruz Baskin School of Engineering José Joaquin Garcia-Luna-Aceves (PI) Hamid R. Sadjadpour • Formal Modeling and Analysis • Automated Reasoning Tools • Semantic Models • Policy Languages • Secure Sensor networks • Resource Management and Scheduling • Dependable System Architecture • Wireless, mobile Ad-Hoc networks • Fault-Tolerant Internetworking • Multipoint communication • Communications and Signal Processing

  3. BS FS KB RIDE/SPINDLE Embedding Application RoutePlanning BundleStore Control BundleForwarding Forwarding KnowledgeManagement KnowledgeBase ResourceManagement FrameStore Convergence Layer Bundles/Frames Link Status Knowledge Trigger

  4. RIDE Algorithms • Opportunistic Message Switching • Takes into account content and resistance • Coordinated Resource Scheduling • Maintain a virtual traffic infrastructure • Reflective route planning • Generate robust routing plans • Distributed Information Management • Uniformly manage routing-related information

  5. Opportunistic Message Switching • Accomplishments • Analyzed mobility-capacity-delay tradeoffs of wireless networks in which nodes store and carry packets before delivery to destinations R. de Moraes, H. Sadjadpour, and J.J. Garcia-Luna-Aceves, ``Mobility-Capacity-Delay Trade-off in Wireless Ad Hoc Networks,'' Ad Hoc Networks Journal, to appear. • Developed the Space-Content-adaptive-Time Routing (SCaTR) framework, which enables data delivery in the face of temporary and long-lived MANET partitions. • Implemented SCaTR by extending AODV: • Proxy takes custody of bundle if no direct route available, e.g. due to disconnection/disruption • Showed that the performance of SCaTR is better than on-demand and epidemic routing using simulations in GloMoSim with scenarios of varying degrees of random or predicted connectivity.

  6. Coordinated Resource Scheduling • Accomplishments • Special-purpose Java simulator inspired by DTNRG simulator allows random and predefined dynamic topologies • Design and Java prototype of a baseline algorithm for resource management based on a virtual infrastructure • Extension of network simulation model with a physical resource schedule based on annotated Petri nets

  7. Reflective Route Planning • Accomplishments • Preliminary Definition of Knowledge Representation and Routing Plans • Baseline Planning Algorithm based on Symbolic Search in Maude • Interoperation between Java Simulator and Maude Planner 2 ? 1

  8. Milestones and Costs Technical reports: • End of August 05 • Design of Models and Information Exchange Algorithms • Preliminary prototype in Java: • Distributed information management • End of April 06 • Final designs and prototypes in Java/Maude: • Distributed information management • Reflective route planning • Coordinated resource scheduling • Final design and prototype in GloMoSim: • Opportunistic message switching • Funds Status: • Spending slightly below expectations due to delays with subcontracts • Transition of Mark-Oliver Stehr to SRI will enhance collaboration and SRI subcontract will be increased correspondingly • No changes in total cost and timeline Preliminary report after 6 months Final reports after 14 months

  9. Go/No Go Criteria • In scenarios with 20% availability and 80% utilization bundles will be delivered eventually (i.e. 100% reliability) assuming buffers are sufficiently large and network is sufficiently connected • Tradeoff between reliability and storage space will be improved by distributed mechanisms to discard superfluous bundles • In scenarios with local congestion, resource management will reduce congestion and improve delivery rate • In scenarios with predictable behavior, route planning will improve delivery rate compared with a base line shortest path routing algorithm

  10. Highlights after Phase I • Reduction of congestion due to active resource management • Show superiority of opportunistic message switching approach compared to traditional routing and epidemic approaches. • Use of a formal planning engine as a core component for reflective routing • integrates multiple routing algorithms and • increases robustness of routing

  11. Towards Phase II • Expected Accomplishments after Phase I • New resource-driven paradigm for routing • Unique combination of opportunistic and formal planning-based routing • Remaining Problems after Phase I • For a single algorithm: Parameter selection and adaptation • For multiple algorithms: Algorithm selection, collaboration, overall interoperation • Lack of Solutions for Security and Trust • Lack of integration between MAC and network layers • Limited support for multipoint communication with different degrees of end-to-end reliability.

  12. Towards Phase II • Three-pronged approach to address remaining problems • Leverage UIUC/SRI’s expertise onformal methods and AI technology -> build upon ongoing work at SRI to integrate these two • Leverage UIUC/SRI’s expertise on formal approaches to network security -> propose new approach to address security and trust issues - Leverage UCSC/SRI‘s expertise on routing algorithms and multipoint communication -> propose algorithms for multipoint communication and enhance cross layer integration

  13. Preliminary Results

  14. RIDE: Hello Window • These values are averaged over a longer period (Hello Window). • Node maintains the past n values, and averages them to produce its current contact value.

  15. RIDE: Simulations • SCaTR framework is added to AODV in GloMoSim. • Compared to flooding, controlled flooding, AODV, AODV with source buffering. • Several Mobility Scenarios:

  16. RIDE: Delivery Rate (Varied Scenario Size)

  17. RIDE: Delivery Rate (Varied Scenario Length)

  18. RIDE: Control Overhead (Varied Scenario Size)

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