potential-based entropy adaptive routing for disruption tolerant networks hideya ochiai hiroshi...
TRANSCRIPT
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Potential-Based Entropy Adaptive Routingfor Disruption Tolerant Networks
Hideya Ochiai Hiroshi EsakiThe University of Tokyo / NICT
DTNRG, IETF 76 Hiroshima, 2009-11-13
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Outline
• Introduction• Potential-based entropy adaptive routing• Prototype implementation• Campus-wide experiment• Conclusion
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Introduction• DTN for opportunistic networking
– Isolated sensor networking / vehicular ad-hoc networks– Intermittent connectivity / isolated networks
• API– void sendMessage(ID dst, String msg);– void recvMessage(ID src, String msg);
• All the nodes are always virtually connected.
1
34
2
5
7
6Intermittent Connectivity
8
Isolated Networks
Mobile Node
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Contributions of this work
Implementation and deployment of potential-based entropy adaptive routing (PEAR)
– PEAR autonomously enables message delivery in ad-hoc manner.
– In general, the performance of routing algorithms are strongly dependent on mobility patterns.
– PEAR dynamically adapts to wide-range of mobility patterns without being aware of mobility pattern itself.
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Outline• Introduction• Potential-based entropy adaptive routing• Prototype implementation• Campus-wide experiment• Conclusion
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Message Delivery
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PossibleIntermediate Nodes
Small entropy case Large entropy casePotential-field
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Characterize Mobility Pattern by Entropy
Small Entropy Case Large Entropy CaseLocally Distributed Widely Distributed
Delivery by hop-by-hop forwarding Delivery by the movement of the nodes
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Entropy and Delivery Pattern
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Small Entropy Case Large Entropy Case
A bolder link indicates larger contact time.
Biased contact Uniform contact
It should choose the best path It should improve delivery rate by increasing redundancy.
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How does PEAR achieve that ??
• For choosing the next hop node:– Potential-based routing– Potential-field construction (inspired by diffusion
theory)
• For message delivery– Copy-based message delivery
Transfer-based message delivery
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SensorTruck
InternetGateway
Live E!
To deliver sensor readings to the Internet GW
Potential
Data Flow
Data Flow
: Wireless Device
Potential-Based Routing
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Potential-Field Construction in PEAR
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Potential-Field Construction
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Diffusion Equation
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Potential-Field Construction
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ConstructedPotential-Field
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Potential and Message Routing
Small Entropy Case
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Message Delivery
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Potential and Message Routing
Large Entropy CaseD
istr
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IntermediateNodes
Source Node
Destination Node
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DestinationNode
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PossibleIntermediate Nodes
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Transfer-Based v.s. Copy-Based Message Delivery
Transfer-BasedMessage Delivery
Copy-BasedMessage Delivery
PEAR
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Copy-Based Message Transfer in PEAR
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For each k in nexthopstat := check_message_status(k,m)if stat = NEIGHBOR_NOT_FOUND then
continue
if stat = MESSAGE_DELIVERED thendelete_content(m)m.IsDelivered:=truecontinue
if m.DisseminationTTL>0 Thenif stat = NOT_HAVE Then
copy(k,m)
if m.DisseminationTTL > DISSEMINATION_MODE_TIME thenm.DisseminationTTL :=DISSEMINATION_MODE_TIME
Do you have any information about msg_id= XXXX ??
• NOT_HAVE• ALREADY_HAVE• DELIVERED
Algorithm
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Rough Simulation-Based Evaluation
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Delivery Rate
100%
Mobility Pattern
PEAR
Link State Based
Spray and Wait
Rough Simulation-Based Evaluation (Summary)
Village-to-village Human-to-human
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Outline• Introduction• Potential-based entropy adaptive routing• Prototype implementation• Campus-wide experiment• Conclusion
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Software Design of PEAR
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Potential Table Nexthop Table
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InvestigationAbout 3000 lines in C.Footprint is 34k byte in object code.
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Armadillo220
Battery(6.0V 2100mAh)Power Circuit
Wifi 802.11gStorage(2GByte)
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Armadillo-220
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Outline• Introduction• Potential-based entropy adaptive routing• Prototype implementation• Campus-wide experiment• Conclusion
Sensor2
Sensor1
電気系会議室3
Group1
Group2
Group3
Gateway
50m
Contact graph Experiment settingsDelivery pattern
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Members for the experiment
• The University of Tokyo– Ochiai, Shimotada, Fujita, Kawakami, Himura,
Sugita, Lert, Wan, Minshin, Motodate, Kure, Kawaguchi, Ishizuka
• Nara Institute of Science and Technology– Dr. Matsuura
• Keio University– Dr. Miyakawa, Yamanouchi
• Cisco Systems, Inc.– Momose
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Scenario Overview
Wifi Interface (USB)
Storage (USB)
Armadillo-220
リチウムイオン蓄電池
Wifi Interface (USB)
Storage (USB)
Armadillo-220
リチウムイオン蓄電池
Wifi Interface (USB)
Storage (USB)
Armadillo-220
リチウムイオン蓄電池
Wifi Interface (USB)
Storage (USB)
Armadillo-220
リチウムイオン蓄電池
Wifi Interface (USB)
Storage (USB)
Armadillo-220
リチウムイオン蓄電池
SensorStatic (#1)Wifi Interface (USB)
Storage (USB)
Armadillo-220
リチウムイオン蓄電池
SensorStatic (#2)
InternetGW (#99)
Mobile Node (#3)
Mobile Node (#4)
Mobile Node (#5)
1. Deploy sensors in remote sites.2. Sensors periodically transmit data to the GW3. Mobility of nodes enables delivery of data from sensors to the GW.
Wifi Interface (USB)
Storage (USB)
Armadillo-220
リチウムイオン蓄電池
Wifi Interface (USB)
Storage (USB)
Armadillo-220
リチウムイオン蓄電池
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Sensor2
Sensor1
電気系会議室3
Group1
Group2
Group3
Gateway
50m
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The number of received neighbor advertisement packets.e.g., node 8 received 45 advertisement from sensor 1.
Contact Graph
Mobility Entropy 2.1≒
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Potential for the Gateway
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Message Flow (1/2)
Red arrow: messages from sensor 1 (#1)Blue arrow: messages from sensor 2 (#2)GW: #99
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Message Flow (2/2)
The number along with an arrow indicates the time of the transfer
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Distribution of Delivery Latency
E.g., 50 = [0,100]
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Collected Temperature Data from Sensor 1
Delivery Rate = 100 %
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Collected Temperature Data from Sensor 2
Delivery Rate = 100 %
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Outline
• Introduction• Potential-based entropy adaptive routing• Prototype implementation• Campus-wide experiment• Conclusion
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Conclusion• We proposed PEAR for opportunistic networking
– It directly forwards a message at small entropy cases– It replicates a message to improve delivery latency at
large entropy cases
• Implementation and deployment of PEAR– Prototype system with embedded computers– 10-node scale campus wide experiment
• PEAR has achieved 100% delivery rate with reasonable delay on the experiment settings.
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Thank you...
Armadillo220
Battery(6.0V 2100mAh)Power Circuit
Wifi 802.11gStorage(2GByte)
Dis
trib
utio
nP
oten
tial
DestinationNode
SourceNode
Intermediate Nodes
Source
DestinationIntermediate
tnVtkVDtnVtnV dd
nnbrk
dd ,,min,1,)(
Message Delivery
Dis
trib
utio
n
IntermediateNodes
Source Node
Destination Node
SourceNode
DestinationNode
Dir
ect
Pat
h
Pot
entia
l
tnVtkVDtnVtnV dd
nnbrk
dd ,,min,1,)(
PossibleIntermediate Nodes
Delivery Rate
100%
Mobility Pattern
PEAR
Link State Based
Spray and Wait
Google by "Mobility Entropy and Message Routing"
Small entropy case Large entropy case
PEAR maintains high delivery rate over wide-range of mobility patternsPrototype implementation