Enhancing DTN capacity Enhancing DTN capacity with Throwboxeswith Throwboxes
(work-in-progress)(work-in-progress)
Wenrui Zhao, Yang Chen, Mostafa Ammar, Mark Corner,
Brian Levine, Ellen Zegura
Georgia Institute of TechnologyUniversity of Massachusetts Amherst
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
Capacity Limitation in DTNs
DTNs are intermittently connected Potentially low throughput, large delay
Question: enough capacity for applications? What if not?
Use radios with longer range Deploy a mesh network as infrastructure Message ferrying
This presentation: Throwboxes
Enhancing DTN Capacity
MF
SM
D
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]
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
Throwboxes
Processor Intel PXA255 400MHz
Memory 64MB SDRAM
32MB Flash
Power consumption
< 500mA
Size 3.5’’ x 2.5’’
Weight 47g
Example: DTN w/out Throwboxes
Example: DTN w/ Throwboxes
UMassDiesel DTN Example
w/out TB
w/ TB
Total contact duration (sec)
631 11927
Effective capacity (Kbps)
3.5 66.3
Delay (sec) 63012 3120
Data transmission between bus 38 and bus 45 A single throwbox achieves an improvement factor of 19 for
both capacity and delay
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throw-boxBus 30Bus 31Bus 34Bus 35Bus 36Bus 38Bus 39
Bus 39eBus 45
Main Question
How to best deploy ‘s Where? How to route through them? When? -- Later work
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
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
1,
ji
ijb
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)
Throwbox Deployment & Routing Framework
Contactoblivious
Contactbased
Traffic & Contact based
Multi-pathrouting
Single pathrouting
Epidemic routing
Deployment approach
Routing approach
Random or Regular Deployment
Throwbox Deployment & Routing Framework
Contactoblivious
Contactbased
Traffic & Contact based
Multi-pathrouting
Single pathrouting
Epidemic routing
Deployment approach
Routing approach
Random or Regular Deployment
Multi-Path Routing – Multi-Path Routing – Traffic and Contact-Aware DeploymentTraffic and Contact-Aware Deployment
Need to determine Deployment locations of throwboxes Routing paths and traffic load on each path
Performance objective Given m throwboxes, maximize total throughput λ
such that traffic load λbij is supported from node i to j
Multi-Path Routing – Multi-Path Routing – Traffic and Contact-Aware Deployment 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
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=1 to m do(2) find location L that maximizes λ (3) deploy a throwbox at location L(4) end(5) compute routing
Throwbox Deployment & Routing Framework
Contactoblivious
Contactbased
Traffic & Contact based
Multi-pathrouting
Single pathrouting
Epidemic routing
Deployment approach
Routing approach
Random or Regular Deployment
Multi-Path Routing – Multi-Path Routing – Contact-Based DeploymentContact-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
Throwbox Deployment & Routing Framework
Contactoblivious
Contactbased
Traffic & Contact based
Multi-pathrouting
Single pathrouting
Epidemic routing
Deployment approach
Routing approach
Random or Regular Deployment
Single Path RoutingSingle 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
Throwbox Deployment & Routing Framework
Contactoblivious
Contactbased
Traffic & Contact based
Multi-pathrouting
Single pathrouting
Epidemic routing
Deployment approach
Routing approach
Random or Regular Deployment
Epidemic RoutingEpidemic 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
Performance Evaluation
Objectives Utility of throwboxes in performance enhancement Performance impact of various routing and deployment approaches
ns simulationdeployment/routing
computation
traffic demand
node mobility
throwbox locations
routing path/load
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
Delivery Ratio vs. Number of Throwboxes
Multi-path routing
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Number of throw-boxes
T &C AwareAbsoluteContactRelativeContact
RandomGrid
Delivery Ratio vs. Number of Throwboxes
Single path routing
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0.25
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0.35
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0.45
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live
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Number of throw-boxes
T & C AwareAbsoluteContactRelativeContact
RandomGrid
Delivery Ratio vs. Number of Throwboxes
Epidemic routing
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0.15
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0.25
0.3
0.35
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Me
ssa
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live
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Number of throw-boxes
MultiPathProportional
AbsoluteContactRelativeContact
RandomGrid
Delay vs. Number of Throwboxes(High Traffic Load)
Multi-path routing
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10000
12000
14000
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ssa
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lay
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Number of throw-boxes
T & CAbsoluteContactRelativeContact
RandomGrid
Delay vs. Number of Throwboxes(Low Traffic Load)
Multi-path routing
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2000
3000
4000
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6000
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Number of throw-boxes
T & C AbsoluteContactRelativeContact
RandomGrid
Summary of Simulation Results
RWPmobility
Manhattanmobility
UMassmobility
Multi-pathrouting
Single pathrouting
Epidemic routing
Delay improvement(high traffic load)
Throughput improvement
Delay improvement(low traffic load)
Contact based
T & C
Multi-pathrouting
Single pathrouting
Epidemic routing
Contactoblivious
T & C/Contact based
T & C /Contact based
Contactoblivious
Contactoblivious
High
Low
Throughputimprovement
Routingapproach
Summary of Simulation Results (2)
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
Questions?
Message Ferrying
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