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

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.

•What will happen when the Internet is not always available?

Previous Solutions

• Infostation-based approaches– Mobile Hotspots [19]– Ott ’06 [27]

• But,– Dedicated Infostations needed– Single point of failure and scalability

problems

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.

Our Assumption

• All peers are collaborative.• All peers have local connectivity

– WiFi, Bluetooth, etc.

• All peers are mobile.• Some peers have Internet access.

Internet

FTP

M-FTP: Scenario 1

Gateway Peer:A peer who can

access the Internet directly

M-FTP : Scenario 2a

Vanilla Peer (A):

Peer that cannot access Internet

directly

Gateway Peer (B)

M-FTP : Scenario 2b

Vanilla Peer (A)

Vanilla Peer (B)

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

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).

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

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)

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

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.

UCSD scenario

γ= 20%

γ= 60%

iMote scenario

γ= 20%

γ= 60%

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

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.

Thank You!