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The Phoenix Project

The Phoenix Project. Robin Kravets Tarek Abdelzaher Department of Computer Science University of Illinois. Consider the aftermath of a natural disaster No power Damaged communication infrastructure Goal Survivable communication and networking in post disaster scenarios

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The Phoenix Project

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  1. The Phoenix Project Robin Kravets Tarek Abdelzaher Department of Computer Science University of Illinois

  2. Consider the aftermath of a natural disaster No power Damaged communication infrastructure Goal Survivable communication and networking in post disaster scenarios Research Challenges Heterogeneous devices Diverse resource availability Diverse mobility patterns The Phoenix Project The-Day-After Networks

  3. Project Goals • Protocol Stack to support • Disconnected operation • Service oriented communication • Resource limited environments • Approach • Exploit topology and mobility characteristics • Adapt protocol behavior to changing conditions Police The-Day-After Networks

  4. Designing Protocols for DTNs • Challenge • Lack of models to describe network topology and mobility • Question • What high-level principles guide the design of efficient routing protocols for post-disaster DTNs? The-Day-After Networks

  5. Design Principles • Conservation of popularity • A node that has been popular in the recent past will continue to be popular in the near future • Ties into the role of the node in the network • Police-officer, rescue worker, etc • Recurrence • Many network nodes tend to perform recurrent activities • Police patrols, supply shuttles, ambulances • Clustering • Nodes tend to cluster • Due to mobility • At aggregation points (supply centers, distress scenes, etc) The-Day-After Networks

  6. Routing Quota-based protocols reduce resource usage Blindly distribute quota Approach Push quota to more popular nodes Protocol Every node maintains an encounter value (EV) Exchange EVs on contact Quota transmitted is in proportion to EV ratio Leveraging Popularity: Encouter-Based Routing The-Day-After Networks

  7. Routing Quota-based protocols reduce resource usage Blindly distribute quota Approach Push quota to more popular nodes Protocol Every node maintains an encounter value (EV) Exchange EVs on contact Quota transmitted is in proportion to EV ratio Leveraging Popularity: Encouter-Based Routing The-Day-After Networks • EBR • Resource efficient • Low overhead / state • Low complexity • High message delivery ratio

  8. Leveraging Recurrence: Inter-Contact Routing C B • Dynamic, yet predictable behavior • Nodes may frequently meet a few other nodes at predictable times • New routing Metric: Inter-contact delay • Track recurrent contact times • Construct ‘paths’ with high delivery probability A 5 B C The-Day-After Networks 20 min loop A Encounter graph IC-Routing 15 AB AC 20 20 5

  9. Leveraging Recurrence: Inter-Contact Routing • Dynamic, yet predictable behavior • Nodes may frequently meet a few other nodes at predictable times • New routing Metric: Inter-contact delay • Track recurrent contact times • Construct ‘paths’ with high delivery probability The-Day-After Networks • Inter-Contact Routing • Reduce end-to-end delay • Save resources by reducing the number of replicas

  10. Leveraging Clustering: Mercury • Observation • Clustering occurs even in partitioned networks • Augment store-carry-forward routing with path-based routing • Base routing mechanism: Hop-by-hop opportunistic forwarding • Use end-to-end routing when available • Light-weight clustering • Route discovery throughout lifetime of message • Inter-cluster communication when nodes move in groups Single hop connection opportunity The-Day-After Networks Multi hop connection opportunity

  11. Leveraging Clustering: Mercury • Observation • Clustering occurs even in partitioned networks • Augment store-carry-forward routing with path-based routing • Base routing mechanism: Hop-by-hop opportunistic forwarding • Use end-to-end routing when available • Light-weight clustering • Route discovery throughout lifetime of message • Inter-cluster communication when nodes move in groups Mercury: Improved delivery ratio Low control overhead The-Day-After Networks

  12. Putting It All Together • Target network is dynamic • Valid principles change over time • Approach: Adaptive Routing • Dynamically use principles that are valid • Routing layer is composed of several routing experts • Routing expert focus on a specific principle • Convert expert routing information to a common metric • Routing decisions consult all experts • Experts with valid assumptions provide high-confidence paths The-Day-After Networks

  13. Observation Congestion is a global condition Nodes only have local (neighborhood) information Dynamically adjust replication rate Based on current network conditions Collect drop, duplicate delivery and message hop statistics Compare ratio of good (dups) over bad (drops) against congestion threshold Congestion Decrease Resource Management: Congestion Control The-Day-After Networks

  14. Environment: Mobility Modeling • Current models • All nodes to follow the same behavior • Persistent behavior • Observations • Object movement is heavily dependent on events • Object reactions are completely dependent on the current role of the object • High-level framework • Event-driven • Events directly change movement patterns • Role-based • Nodes assume roles, which react to events by changing their movement patterns The Phoenix Project

  15. Additional Research Directions • Context-awareness • Survivor-activity recognition and distress situation detection • Application automatically identifies likely distress • Device sends an SOS signal for help • Energy saving strategies • Content batching to reduce total energy consumption • Use of heterogeneous radios to improve communication energy efficiency The Phoenix Project

  16. The Phoenix Project Robin Kravets Tarek Abdelzaher Department of Computer Science University of Illinois

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