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Integration of Application-Layer Scheduling and Routing in Delay-Tolerant MANETs

Integration of Application-Layer Scheduling and Routing in Delay-Tolerant MANETs. José Brustoloni, Sherif Khattab , Christopher Santamaria, Brian Smyth, and Daniel Mossé C S @ P I T T. Mobile Ad-Hoc Networks (MANETs). Cell phone. Mobile Ad-Hoc Networks (MANETs).

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Integration of Application-Layer Scheduling and Routing in Delay-Tolerant MANETs

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  1. Integration of Application-Layer Scheduling and Routing in Delay-Tolerant MANETs José Brustoloni, Sherif Khattab, Christopher Santamaria, Brian Smyth, and Daniel Mossé CS@PITT

  2. Mobile Ad-Hoc Networks (MANETs) Cell phone

  3. Mobile Ad-Hoc Networks (MANETs)

  4. Communication of First-Responders • Connecting handheld devices used to exchange data, images, video, voice, work orders, etc.

  5. Need for MANETs • MANETs are needed if traditional communication infrastructure is damaged

  6. Network Partitioning • MANETs may get partitioned: • Field characteristics • Noisy environments Packethop.com

  7. Delay-Tolerant Networking • DTN research deals with routing of messages across network partitions • Our work proposes a new approach to DTN routing

  8. Example Scenario Main Partition Subordinate Partition Leader EOC

  9. Example Scenario Main Partition Subordinate Partition Work Order Leader EOC

  10. Example Scenario Main Partition Subordinate Partition Work Order Courier Leader EOC

  11. Work Order Model Execution Time Deadline Pre-emption Deadline Miss Time

  12. Work Order Model Each task associated with a location Deadline

  13. Courier Selection Problem Main Partition Subordinate Partition ? ? ? Work Order Courier Leader EOC

  14. Metrics • Percentage of missed deadlines = • Average traveled distance per node Number of deadlines missed Total number of work orders

  15. State-of-the-art • Dedicated mobile elements • Message Ferries [@GeorgiaTech] handle only message delivery • Minimize average delay • Trajectory modification of mobile users • @Dartmouth [Mobicom’00] • Minimize detour distance

  16. Our Hypothesis • We can achieve better trade-off between missed deadlines and traveled distance if application-layer demand is taken into consideration in courier selection

  17. Highest-Slack Courier Selection Main Partition Subordinate Partition Maximum Leeway

  18. Compared Schemes • Closest • Select courier closest to work order destination • Dedicated • Set of nodes dedicated for message delivery (don’t execute any work) • Random

  19. Common Assumption • Leader aware of current position of main-partition workers • GPS-enabled devices • Landmarks

  20. Simulation Parameters • Rate of work orders (load) • main and sub-ordinate • default = 60% • Distance between partitions • default = 1200m • Number of dedicated couriers • default = 1 • Speed of dedicated couriers • default = 5 m/s (18 km/h)

  21. Distance between Partitions 100% Dedicated Random Closest Highest-Slack 80% 60% Deadlines Missed 40% 20% 1200m 200m 400m 800m

  22. Subordinate-partition Load 100% Random Closest Dedicated Highest-Slack 80% 60% Deadlines Missed 40% 20% 20% 40% 60% 80%

  23. Why? Main Partition Subordinate Partition

  24. Subordinate-partition Load 16km 12km Random Traveled Distance Per Node 8km Closest Highest-Slack 4km Dedicated 20% 40% 60% 80%

  25. Main-partition Load 100% Dedicated Random Closest Highest-Slack 80% 60% Deadlines Missed 40% 20% 20% 40% 60% 80%

  26. Dedicated-courier Speed 100% Random Closest Dedicated Highest-Slack 80% 60% Deadlines Missed 40% 20% 54 km/h 18 km/h 25 km/h

  27. Number of Dedicated Couriers 100% Random Closest Dedicated Highest-Slack 80% 60% Deadlines Missed 40% 20% 5 1 10 15 20

  28. Conclusions and Future Work • Courier Scheduling in partitioned ad-hoc networks • Integrated application- and network-layer scheduling • More realistic • models of work orders • metrics (e.g., rate of casualties) • frequency and structure of network partitions • Comparison with other schemes • communication bridges

  29. Questions ?

  30. Work Order Parameters • Average Deadline = 440 sec • Execution Time = 0.5 * Deadline • Enough to run back and forth across a 500m partition and still meet deadline

  31. Simulation Time Cool-down Statistics Gathering Warm-up 10000 (~ 2.5 Hrs) 1000 10 9500 Time (Seconds)

  32. When to return home? Main Partition Subordinate Partition

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