1 / 37

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

kenley
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related