a peer-to-peer approach for mobile file transfer in opportunistic people networks
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A Peer-to-Peer Approach for Mobile File Transfer in Opportunistic People Networks. Ling-Jyh Chen and Ting-Kai Huang Institute of Information Science, Academia Sinica, Taiwan. Motivation. Internet is part of our lives We can use the Internet “almost” anywhere/ anytime. Cellular - PowerPoint PPT PresentationTRANSCRIPT
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A Peer-to-Peer Approach for Mobile File Transfer in Opportunistic People
Networks
Ling-Jyh Chen and Ting-Kai HuangInstitute of Information Science, Academia Sinica, Taiwan
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Motivation
• Internet is part of our lives
• We can use the Internet “almost” anywhere/ anytime.– Cellular– Wi-Fi Hotspots
• Even with Mobility, we have handover solutions.
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•What will happen when the Internet is not always available?
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Previous Solutions
• Infostation-based approaches– Mobile Hotspots [19]– Ott ’06 [27]
• But,– Dedicated Infostations needed– Single point of failure and scalability
problems
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Our Contribution
• We proposed M-FTP to improve the effectiveness of FTP application in mobile opportunistic networks.
• Every peer can access the Internet when parts of them have internet access.
• Proposed a “Collaborative Forwarding algorithm” to further utilize opportunistic ad hoc connections and spare storage in the network.
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Our Assumption
• All peers are collaborative.• All peers have local connectivity
– WiFi, Bluetooth, etc.
• All peers are mobile.• Some peers have Internet access.
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Internet
FTP
M-FTP: Scenario 1
Gateway Peer:A peer who can
access the Internet directly
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M-FTP : Scenario 2a
Vanilla Peer (A):
Peer that cannot access Internet
directly
Gateway Peer (B)
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M-FTP : Scenario 2b
Vanilla Peer (A)
Vanilla Peer (B)
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B rcv A’s request
Direct forwarding
Collaborative forwarding
IndirectForwarding
Do nothingRequest
Forwarding
The request has been relayed H times
B has the Requested file
B and A are connected
B and A are connected
B is a GPY
Y
N
Y
Y
N
N
N
NY
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Collaborative Forwarding Algorithm
• Goal: Increase the packet delivery ratio and decrease the request response time
• Method: – PROPHET [22]
• Based on Epidemic Routing Scheme [26] • Delivery predictability
– Caching improves hit rate in the future (esp. for popular pages).
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Direct Forwarding vs. Indirect Forwarding
• B has complete content =>Direct Forwarding algorithm
• B may only have partial content =>Indirect Forwarding algorithm– Further passing the request message
using Request Forwarding algorithm
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Evaluations
• Evaluate the performance of M-FTP scheme against Mobile Hotspots scheme– Service ratio and traffic overhead
• DTNSIM: Java-based simulator• Real-world wireless traces
– UCSD (campus trace)– iMote (Infocom ‘05)
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The Properties of two network traces
Trace Name iMote UCSD
Device iMote PDA
Network Type Bluetooth WiFi
Duration (days) 3 77
Devices Participating 274 273
Number of Contacts 28,217 195,364
Avg # Contacts/pair/day 0.25148 0.06834
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Parameter Settings
• Number of GPs– γ mobile peers
• Number of requesters: – 20% of the other peers (VPs)
• Number of requests: – first 10% of simulation time with a Poisson rate
of 1800 sec/request.
• The FTP requests:– top 100 requested iTunes songs , – As report as in iTune store on Sep. 7 2007.
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UCSD scenario
γ= 20%
γ= 60%
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iMote scenario
γ= 20%
γ= 60%
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Traffic Overhead
γM-FTP
(A)Mobile Hotspots
(B)Normalized Overhead
(A/B)
iMote
20% 22,170 5,866 3.78
40% 23,932 6,613 3.62
60% 24,696 7,197 3.43
UCSD
20% 1,425,943 269,834 5.28
40% 1,510,094 261,653 5.77
60% 1,535,310 261,820 5.86
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Conclusion
• We propose the solution, M-FTP, that can provide effective data transfer on the go.– Peer to peer– No dedicated devices
• M-FTP implements a Collaborative Forwarding algorithm that takes advantage of opportunistic encounters.
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Thank You!