Linda Briesemeister, Jos Joaquin GarciaLunesAceves, Hamid R' Sadjadpour, Jos Meseguer, MarkOliver St

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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
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RIDE Team
University of Illinois at Urbana-Champaign Departm
ent 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

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

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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.

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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

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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

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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
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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

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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

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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.

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Towards Phase II

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

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Preliminary Results
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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.

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RIDE Simulations

• SCaTR framework is added to AODV in GloMoSim.

• Compared to flooding, controlled flooding,
  AODV, AODV with source buffering.

• Several Mobility Scenarios

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RIDE Delivery Rate (Varied Scenario Size)
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RIDE Delivery Rate (Varied Scenario Length)
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RIDE Control Overhead (Varied Scenario Size)