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Enhancing DTN capacity with Throwboxes (work-in-progress)

Enhancing DTN capacity with Throwboxes (work-in-progress). Wenrui Zhao, Yang Chen, Mostafa Ammar , Mark Corner, Brian Levine, Ellen Zegura Georgia Institute of Technology University of Massachusetts Amherst. Delay Tolerant Networks (DTN). DTNs: non-Internet-like networks

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Enhancing DTN capacity with Throwboxes (work-in-progress)

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  1. Enhancing DTN capacity with Throwboxes(work-in-progress) Wenrui Zhao, Yang Chen, Mostafa Ammar, Mark Corner, Brian Levine, Ellen Zegura Georgia Institute of Technology University of Massachusetts Amherst

  2. Delay Tolerant Networks (DTN) • DTNs: non-Internet-like networks • Intermittent connectivity • Large delays • High loss rates • Examples of DTNs • Tactical networks, disaster relief, peacekeeping • Interplanetary networks, rural village networks • Underwater acoustic networks • DTN features • Store-Carry-and-forward • Message switching

  3. Capacity Limitation in DTNs • DTNs are intermittently connected • Potentially low throughput, large delay • Question: enough capacity for applications? • What if not?

  4. MF S M D Enhancing DTN Capacity • Use radios with longer range • Deploy a mesh network as infrastructure • Message ferrying • This presentation: Throwboxes

  5. Our Work on MF/DTN • Ferry Route Design Problem[FTDCS 03] • MF with Mobile Nodes [MobiHoc 04] • Efficient use of Multiple Ferries[INFOCOM 05] • The V3 Architecture: V2V Video Streaming[PerCom 05] • Ferry Election/Replacement [WCNC 05] • MF as a power-savings device [PerCom 05] • Multipoint Communication in DTNs/MF [WDTN 05, WCNC 06] • Power Management Schemes in DTNs/MF [SECON 05, PerCom 05] • Road-side to Road-side relaying using moving vehicles [WCNC 06]

  6. Throwboxes • Basic idea: add new devices to enhance data transfer capacity between nodes • Deploy throwboxes to relay data between mobile nodes • Throwboxes are: • small, inexpensive, possibly dispensable, battery-powered wireless devices • Some processing and storage capability • Easy to deploy and replenish

  7. Throwboxes

  8. Example: DTN w/out Throwboxes

  9. Example: DTN w/ Throwboxes

  10. UMassDiesel DTN Example • Data transmission between bus 38 and bus 45 • A single throwbox achieves an improvement factor of 19 for both capacity and delay

  11. Main Question • How to best deploy ‘s • Where? • How to route through them? • When? -- Later work

  12. Throwbox Deployment & Routing Framework • Objective: throughput enhancement • Important to deliver data • May improve delay too • Deployment issue • Where to place throw-boxes? • Routing issue • How data are forwarded? • Contact-oblivious • Contact-based • Traffic and Contact based • Single path routing • Multi-path routing • Epidemic routing

  13. Network Model • DTN consists of mobile nodes • Relative traffic demand between nodes bij • Total throughput λ • Given inherent capacity (w/out TBs) as a function of: • Contacts – dictated by mobility patterns • Data rate

  14. Throwbox Assumptions • Sufficient energy supplies • No interaction between throwboxes • Deployed to a given set of potential locations • Center of Grid Cells • Deployment Vector (0/1 vector)

  15. Throwbox Deployment & Routing Framework Deployment approach Traffic & Contact based Contact based Contact oblivious Random or Regular Deployment Routing approach Multi-path routing Single path routing Epidemic routing

  16. Throwbox Deployment & Routing Framework Deployment approach Traffic & Contact based Contact based Contact oblivious Random or Regular Deployment Routing approach Multi-path routing Single path routing Epidemic routing

  17. Multi-Path Routing – Traffic and Contact-Aware Deployment • Need to determine • Deployment locations of throwboxes • Routing paths and traffic load on each path • Performance objective • Given mthrowboxes, maximize total throughput λsuch that traffic load λbij is supported from node i to j

  18. Multi-Path Routing – Traffic and Contact-Aware Deployment • Formulated as an 0/1 linear programming problem • Throwbox deployed at location  1 • Solution also gives optimal flow vector describing use of multiple paths • NP-hard to solve optimally

  19. Greedy Heuristic • Deploy throwboxes one by one • Given throwbox locations, (2) is a concurrent flow problem • Solved by network flow techniques or linear programming tools (1) for i=1tomdo (2) find location L that maximizes λ (3) deploy a throwbox at location L (4) end (5) compute routing

  20. Throwbox Deployment & Routing Framework Deployment approach Traffic & Contact based Contact based Contact oblivious Random or Regular Deployment Routing approach Multi-path routing Single path routing Epidemic routing

  21. Multi-Path Routing – Contact-Based Deployment • Throwbox deployment is based on contact information, but not traffic information • Benefits varying traffic patterns • May not be optimal for specific traffic • Maximize • Absolute contact enhancement • Maximize absolute enhancement of contact between nodes • Relative contact enhancement • Maximize relative enhancement of contact between nodes

  22. Throwbox Deployment & Routing Framework Deployment approach Traffic & Contact based Contact based Contact oblivious Random or Regular Deployment Routing approach Multi-path routing Single path routing Epidemic routing

  23. Single Path Routing • Single path routing • Data for a S-D pair follow a single path • Adapt greedy algorithm for multi-path routing by enforcing the “single path” requirement

  24. Throwbox Deployment & Routing Framework Deployment approach Traffic & Contact based Contact based Contact oblivious Random or Regular Deployment Routing approach Multi-path routing Single path routing Epidemic routing

  25. Epidemic Routing • Epidemic routing (ER) • Difficult to characterize traffic load among nodes because of flooding • ER exploits all paths to propagate data • Multi-path heuristic • Proportional allocation heuristic

  26. traffic demand node mobility deployment/routing computation throwbox locations ns simulation routing path/load Performance Evaluation • Objectives • Utility of throwboxes in performance enhancement • Performance impact of various routing and deployment approaches

  27. Simulation Settings • Node mobility models • Predictable/constrained: UMass model based on measured bus trace • Random/unconstrained: Random waypoint model • Random/constrained: Manhattan model • Simulation Parameters • 9 nodes in a 25Km x 25 Km area • 802.11 MAC, radio range: 250m, bandwidth: 1Mbps • 20 source-destination pairs, message size is 1500 bytes, Poisson message arrival with same data rate • FIFO buffer, buffer size is 50000 messages

  28. 0.8 T &C Aware AbsoluteContact 0.7 RelativeContact Random 0.6 Grid 0.5 Message delivery ratio 0.4 0.3 0.2 0.1 0 0 1 2 3 4 5 6 7 8 Number of throw-boxes Delivery Ratio vs. Number of Throwboxes Multi-path routing

  29. Delivery Ratio vs. Number of Throwboxes 0.45 0.4 0.35 0.3 Single path routing 0.25 Message delivery ratio 0.2 0.15 T & C Aware 0.1 AbsoluteContact RelativeContact 0.05 Random Grid 0 0 1 2 3 4 5 6 7 8 Number of throw-boxes

  30. 0.35 0.3 0.25 0.2 Message delivery ratio 0.15 MultiPath 0.1 Proportional AbsoluteContact RelativeContact 0.05 Random Grid 0 0 1 2 3 4 5 6 7 8 Number of throw-boxes Delivery Ratio vs. Number of Throwboxes Epidemic routing

  31. 14000 12000 10000 8000 Message delay (second) 6000 4000 T & C AbsoluteContact RelativeContact 2000 Random Grid 0 0 1 2 3 4 5 6 7 8 Number of throw-boxes Delay vs. Number of Throwboxes(High Traffic Load) Multi-path routing

  32. Delay vs. Number of Throwboxes(Low Traffic Load) 6000 5000 4000 Multi-path routing Message delay (second) 3000 2000 T & C AbsoluteContact 1000 RelativeContact Random Grid 0 0 1 2 3 4 5 6 7 8 Number of throw-boxes

  33. UMass mobility Throughput improvement Delay improvement (high traffic load) Manhattan mobility Delay improvement (low traffic load) RWP mobility Multi-path routing Single path routing Epidemic routing Summary of Simulation Results

  34. High T & C / Contact based T & C Throughput improvement Contact based T & C/ Contact based Contact oblivious Contact oblivious Contact oblivious Low Routing approach Multi-path routing Single path routing Epidemic routing Summary of Simulation Results (2)

  35. Summary • Study the use of throwboxes for capacity enhancement in mobile DTNs • Develop algorithms for throwbox deployment and routing • Routing: multi-path, single path, epidemic • Deployment: traffic and contact, contact-based, contact-oblivious • Evaluate the utility of throwboxes and various routing/deployment approaches • Throwboxes are effective in improving throughput and delay, especially for multi-path routing and predictable node mobility

  36. Questions?

  37. MF S S M M D D Message Ferrying MF

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