1. 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

2. 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.

3. What will happen when the Internet is not always available?

4. Previous Solutions Infostation-based approaches But,
Mobile Hotspots [19]
Ott ’06 [27]
But,
Dedicated Infostations needed
Single point of failure and scalability problems

5. 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.

6. Our Assumption All peers are collaborative.
All peers have local connectivity
WiFi, Bluetooth, etc.
All peers are mobile.
Some peers have Internet access.

7. A peer who can access the Internet directly
M-FTP: Scenario 1
Internet
FTP
Gateway Peer:
A peer who can access the Internet directly

8. Peer that cannot access Internet directly
M-FTP : Scenario 2a
Gateway Peer (B)
Vanilla Peer (A):
Peer that cannot access Internet directly

9. M-FTP : Scenario 2b
Vanilla Peer (A)
Vanilla Peer (B)

10. B rcv A’s request
B is a GP Y N
B and A are
connected
B has the
Requested file Y N Y N
Direct forwarding
The request has been
relayed H times
B and A are
connected N N
Collaborative
forwarding Y Y
Indirect
Forwarding
Do nothing
Request
Forwarding

11. 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).

12. 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

13. 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)

14. The Properties of two network traces
Trace Name
iMote
UCSD
Device
PDA
Network Type
Bluetooth
WiFi
Duration (days) 3 77
Devices Participating
274
273
Number of Contacts
28,217
195,364
Avg # Contacts/pair/day

15. Parameter Settings Number of GPs Number of requesters:
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

16. UCSD scenario
γ= 20%
γ= 60%

17. iMote scenario
γ= 20%
γ= 60%

18. Traffic Overhead γ M-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
1,425,943
269,834
5.28
1,510,094
261,653
5.77
1,535,310
261,820
5.86

19. 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.

20. Thank You! 22