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Towards efficient content
dissemination over disruption
tolerant networks
PhD Thesis

Candidate: Amir Krifa, INRIA
Supevisor: Chadi Barakat, INRIA

Monday, April 23 2012
Mobile Networking Traffic Growth

Shift
Access to novel applications (social
networks, blogs, music …)

Generation of unprecedented
amounts of mobile data
Complementary architecture ?

Second class
customer
2/39

First class
customer
The DTN concept
Take advantage of increasing mobile nodes resources
Rely on nodes mobility to route messages through
disconnected networks
 A node can be a human carrying a laptop or SmartPhone, a bus, a car, etc

At the opposite of existing networks, no end-to-end path is
required during the communication
 Hop-by-Hop networking
 Message replication

3/39
DTNs: Not as futuristic as it
sounds !

World’s First Flying FileSharing Drones in Action @
GLOW Festival 2011
Netherlands

Wildlife tracking
systems:
ZebraNet, Env.
Monitoring, etc
4/39
Challenges
Challenges:
 Disruption and dynamic environment

-> long-term storage +

replication (Routing Algorithms: Global Optimal, Epidemic, Spry and
Wait, etc …)

RAPID
By Levine et al.

 Long-term storage + replication

– -> buffers congestion (Drop

Policies: Drop Oldest, Drop Last …)

– -> lack of Bandwidth (Scheduling:

FIFO …)

 Mobile devices controlled by rational people
5/39

-> selfishness
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
 Distributed version that works in practice (HBSD)
 Validation results

Point-to-multi-points (Content Centric) communications
 The content centric context
 MobiTrade: optimal resources management solution
 Validation results

Conclusion and Perspectives

6/39
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
 Distributed version that works in practice (HBSD)
 Validation results

Point-to-multi-points (Content Centric) communications
 The content centric context
 MobiTrade: optimal resources management solution
 Validation results

Conclusion and Perspectives

7/39
Methodology
Suppose first global knowledge
Take a global routing metric as the delay or delivery rate
Find what is the best policy to drop and schedule
 Which message should be dropped/scheduled first and that leads to
the best gain in the considered global metric,
 Model this gain as a per-message utility function.

Try to estimate the global knowledge using global
information BUT on old messages …

8/39
Case of delivery rate
Message has the same limited lifetime (TTL)
Suppose global knowledge on m and n
Assumption: meeting times have an exponential tail
In case of congestion, the global delivery rate is :
At least one copy of message i
Will be delivered

Message i is not
delivered yet

m (T )
K(t) K(t) m (T )  
i i  * 1 exp  λn (T )R    i i
DR   P   1
i i1  L 1  
i i i   L 1

i1




Message i will
be delivered
9/39

Message i has been
already delivered
Case of delivery rate
 We differentiate:
k(t) P
i * Δ(n (T ))
Δ(DR)  
i i
i1 ni(Ti)

Δ(n (T ))
i i

-1 : drop
0 : no action
+1 : replication

 GBSD (DR):
The best message to drop is the one having the minimum
partial derivative: 

m (T ) 

1 i i λR exp  λn (T )R 


i i i
L 1  i






And the message to schedule first is the one maximizing
it

10/39
Case of delivery delay
 GBSD (DD):
The best message to drop is the one having the minimum
partial derivative:



1 1 mi(Ti) 




n2(T )λ  L 1 


i i

And the message to schedule first is the one maximizing
it
 For more details:
Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “Message Drop and Scheduling in DTNs: Theory and
Practice”, in IEEE Transactions on Mobile Computing (TMC).
11/39
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
 Distributed version that works in practice (HBSD)
 Validation results

Point-to-multi-points (Content Centric) communications
 The content centric context
 MobiTrade: optimal resources management solution
 Validation results

Conclusion and Perspectives

12/39
Distributed version:
How to calculate n and m ?
n = number of copies of a message
m = number of nodes that have seen the message
Flood information on messages (like in RAPID by UMASS)
 takes long time to converge
 The information is stale by the time it reaches everyone

Our solution:
 Still flood information on messages
 BUT, Estimate n and m at a given elapsed time from what has
happened to old messages at the same elapsed time

13/39
Distributed version (DR)
Suppose m and n follow two random variables M and N

Estimated delivery rate = Mean delivery rate
ˆ
ˆ
m(T)  1 exp  λn(T)R   m(T)  E1 M(T)  1 exp  λN(T)R    M(T)
 
 


ˆ




1
 





 

i   L 1
i   L 1 


L 1  
L 1  













 We set the estimator of m to its expectation (justified by a Gaussion
distribution)

ˆ
m(T)  m (T)  EM T 
  
 




14/39
Distributed version:
Message utility expressions
(H
BS

D)

 For the delivery rate:

M(T)  exp  λR N(T)



λR E 1



i
i


L 1 








ist

2

E L 1M(T)
N(T)
λ L 1 L 1 m (T)

 


 






H

 For the delivery delay:

or
y

Ba

se

d

SD








 Expectation calculated by summing over old messages
15/39
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
 Distributed version that works in practice (HBSD)
 Validation results

Point-to-multi-points (Content Centric) communications
 The content centric context
 MobiTrade: optimal resources management solution
 Validation results

Conclusion and Perspectives

16/39
Validation Setup
 DTN architecture added to the NS-2 simulator
 Random Waypoint and KAIST real mobility trace
 Wireless Range=100m,
 CBR sources, random sources and destinations,
 Each node maintains a buffer with a capacity of 20 messages
Mobility model

KAIST

Random Waypoint

Simulation duration (h):
Simulated Surface (km2):

24
-

7
3*3

Number of nodes:

50

70

Average speed (m/s) :

2

-

TTL (h) :

4

1

1440

360

Interval CBR (s) (10/TTL):

17/39
Delivery Rate
 HBSD outperforms existing protocols (RAPID and Epidemic based on
FIFO/drop-tail) and performs close to the optimal GBSD

Random Waypoint

Almost 60% gain over
RAPID
18/39

KAIST Traces
Close by 14%
How HBSD utilities look like ?
Reduce the number of sources to 15 and decrease the CBR rate of
sources from 10 to 2 messages/TTL (Low congestion regime)

Schedule Youngest First

Drop Oldest

For a lightly loaded network, things are easier and simple policies
can be applied.
19/39
How HBSD utilities look like ?
We fix the number of sources to 50 (high congestion regime)
help the message
over younger ones

penalize – help - penalize
prefer younger ones

For a highly loaded network (complex function)
20/39
Implementation / Web page
And is also available for the DTN2 architecture as an
external router (in C++)
Code has been recently tested in the Scorpion testbed
at the University of California Santa Cruz
Code, papers, presentations are available at:

http://planete.inria.fr/HBSD_DTN2/

21/39
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
 Distributed version that works in practice (HBSD)
 Validation results

Point-to-multi-points (Content Centric) communications
 The content centric context
 MobiTrade: optimal resources management solution
 Validation results

Conclusion and Perspectives

22/39
Previous context: Node Centric
Node Centric vs Content Centric communications
- Source: N2
- Destination: N4

1
2

N2

N5

N1

N4

2
1
1

1

N3

2
1

2

- Source: N1
- Destination: N5
23/39
New context: Content centric
Node Centric vs Content Centric communications
Madonna M. Album
Muse +
Madonna M.
Album

Madonna +
Muse M.
Madonna M.
Album
Album

Track – 1
Track – 2
…

Track – 1
Track – 2
…

Madonna M.
Album

Muse M.
Album

Track – 3
Track – 4
…

Muse Madonna
Muse + M.
Album
M. Album

Track – 1
Track – 2
…

Track – 1
Track – 2
…

Selfish
user !

[Question]: which channels and how
Store Local and a node Channels
much of each shouldForeign carry in !its!
Make Everybody happy ? -> Store Local and Foreign Channels
buffer, Selfishto maximize its future
Block so as users ! (TFT)
reward ??
24/39
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
 Distributed version that works in practice (HBSD)
 Validation results

Point-to-multi-points (Content Centric) communications
 The content centric context
 MobiTrade: optimal resources management solution
 Validation results

Conclusion and Perspectives

25/39
MobiTrade
MobiTrade turns each node into a merchant fetching the content that
has the highest chance to be sold to its good clients
MobiTrade calculates one utility per channel that defines:
 The optimal amount of storage to allocate / channel  Drop policy +
Scheduling policy

Node Storage

Xi
αi B 
B
 Xj
*

j

Channel 1
Channel 2
Channel 3
Channel 4

MobiTrade approximates the Optimal U. based on the amount of
exchanged content per channel @ each meeting while ensuring that
selfish users are blocked

 For more details:

Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “MobiTrade:
Trading Content in Disruption Tolerant Networks”, in proceedings of ACM CHANTS, Las Vegas,
September 2011.
26/39
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
 Distributed version that works in practice (HBSD)
 Validation results

Point-to-multi-points (Content Centric) communications
 The content centric context
 MobiTrade: optimal resources management solution
 Validation results

Conclusion and Perspectives

27/39
Collaborative experimental scenario
 MobiTrade architecture added to the NS-3 simulator
 Synthetic mobility model HCMM
 50 users distributed into 5 groups. The simulation area is divided into a 10*10
grid of cells (5000 meters wide).
 Wireless Range = 60m .
Simulation Scenario
Nbr. Of Users:
Requested CH(s)/User:

50
2

Size of CH(s):

Compare to Podcasting
(PodNet project)

20

Average Delivery Rate (DR): amount of content received for channels a node
requested / total amount of content generated for these channels
28/39
How MobiTrade performs in a
Collaborative scenario ?
MobiTrade efficiently outperforms the two versions of Podcasting
TFT causes a drop in performance among CU

Drop of 6%

Importance of
FC(s)
Almost 2x gain
29/39
Experimental scenarios including
selfish users
Scenarios

SS1

Nbr. Of Users:

40 CU + 10 SU

40 CU + 10SU

CU: 2/20 – SU: 2/10 (SU
and CU channels differ)

CU, SU: 2/20 (among
same channels)

CU: 20 – SU: 40

Requested CH(s):
Size of CH(s):

SS2

CU, SU: 20

We deem such scenarios as the norm rather
than the exception in the real world

30/39
Does MobiTrade keep the system
resources safe ?
Enabling the TFT mechanism blocks selfish users and makes MobiTrade redispatch/reuse the saved resources among the channels shared by
collaborative users
SS1: CU ask for
2/20 channels and
SU ask for 2/10
different channels

Impact on
selfish users

Impact on collaborative users
31/39
Does MobiTrade keep the system
resources safe ?
When TFT is used, the performance of collaborative users is not harmed,
while the one of selfish users drops severely, by up to 2x for a storage of 110
contents.
SS2: each user ask
for 2/20 channels

Impact on SU: Drop by up
to 2x

No Impact on CU
32/39
Implementation / Web page
MobiTrade available for the Android platform
Code, papers, presentations are available at:
http://planete.inria.fr/MobiTrade/
 App Screenshots:

33/39
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
 Distributed version that works in practice (HBSD)
 Validation results

Point-to-multi-points (Content Centric) communications
 The content centric context
 MobiTrade: optimal resources management solution
 Validation results

Conclusion and Perspectives

34/39
Conclusion
A deep study of content sharing in DTN(s) for both:
 Point-to-point communication model
 Point-to-multipoint communication model

New resources management policies in two versions:
 Optimal one that is based on global knowledge
 Practical one that efficient approximate the optimal policy

GBSD/HBSD

MobiTrade

Validation via simulations based on synthetic mobility models and
real mobility traces
Implementation on real word environments (DTN2 and Android)
35/39
Perspectives
MobiTrade
Non altruistic

GB

SD

Collaborative

/HB

Ongoing …

SD

Point-to- point

Point-to-multipoint

36/39
Perspectives
With respect to GBSD/HBSD:
 Tune the utilities of our resources management policies in order to
take into account different messages sizes ...
 Study and design a congestion level detection mechanism to be able
to switch efficiently between resources management policies …

With respect to MobiTrade:
 Implementing the MobiTrade protocol for other types of devices and
experiment with real large scale communities of users...
 Consider more complex content structures …
 Study of the needed mechanisms to control possible advanced
malicious attacks and behaviours that could impair MobiTrade
content sharing sessions ...
37/39
References
Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “MobiTrade: Trading Content in
Disruption Tolerant Networks”, in proceedings of ACM Mobicom Workshop on Challenged
Networks (CHANTS), Las Vegas, September 2011.
Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “Message Drop and Scheduling in
DTNs: Theory and Practice”, in IEEE Transactions on Mobile Computing.
Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, "Optimal Buffer Management Policy
for Delay Tolerant Networks", in proceedings of the 5th IEEE Conference on Sensor,
Mesh and Ad Hoc Communications and Networks (SECON 2008), San Francisco, June 2008.
(CA), June 2008. ---- BEST PAPER AWARD
Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, "An Optimal Joint Scheduling and
Drop Policy for Delay Tolerant Networks”, in proceedings of the WoWMoM Workshop on
Autonomic and Opportunistic Communications, Newport Beach (CA), June 2008.

38/39
Thank you !

39/39

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Towards efficient content dissemination over DTN

  • 1. Towards efficient content dissemination over disruption tolerant networks PhD Thesis Candidate: Amir Krifa, INRIA Supevisor: Chadi Barakat, INRIA Monday, April 23 2012
  • 2. Mobile Networking Traffic Growth Shift Access to novel applications (social networks, blogs, music …) Generation of unprecedented amounts of mobile data Complementary architecture ? Second class customer 2/39 First class customer
  • 3. The DTN concept Take advantage of increasing mobile nodes resources Rely on nodes mobility to route messages through disconnected networks  A node can be a human carrying a laptop or SmartPhone, a bus, a car, etc At the opposite of existing networks, no end-to-end path is required during the communication  Hop-by-Hop networking  Message replication 3/39
  • 4. DTNs: Not as futuristic as it sounds ! World’s First Flying FileSharing Drones in Action @ GLOW Festival 2011 Netherlands Wildlife tracking systems: ZebraNet, Env. Monitoring, etc 4/39
  • 5. Challenges Challenges:  Disruption and dynamic environment -> long-term storage + replication (Routing Algorithms: Global Optimal, Epidemic, Spry and Wait, etc …) RAPID By Levine et al.  Long-term storage + replication – -> buffers congestion (Drop Policies: Drop Oldest, Drop Last …) – -> lack of Bandwidth (Scheduling: FIFO …)  Mobile devices controlled by rational people 5/39 -> selfishness
  • 6. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 6/39
  • 7. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 7/39
  • 8. Methodology Suppose first global knowledge Take a global routing metric as the delay or delivery rate Find what is the best policy to drop and schedule  Which message should be dropped/scheduled first and that leads to the best gain in the considered global metric,  Model this gain as a per-message utility function. Try to estimate the global knowledge using global information BUT on old messages … 8/39
  • 9. Case of delivery rate Message has the same limited lifetime (TTL) Suppose global knowledge on m and n Assumption: meeting times have an exponential tail In case of congestion, the global delivery rate is : At least one copy of message i Will be delivered Message i is not delivered yet m (T ) K(t) K(t) m (T )   i i  * 1 exp  λn (T )R    i i DR   P   1 i i1  L 1   i i i   L 1  i1   Message i will be delivered 9/39 Message i has been already delivered
  • 10. Case of delivery rate  We differentiate: k(t) P i * Δ(n (T )) Δ(DR)   i i i1 ni(Ti) Δ(n (T )) i i -1 : drop 0 : no action +1 : replication  GBSD (DR): The best message to drop is the one having the minimum partial derivative:   m (T )   1 i i λR exp  λn (T )R    i i i L 1  i     And the message to schedule first is the one maximizing it 10/39
  • 11. Case of delivery delay  GBSD (DD): The best message to drop is the one having the minimum partial derivative:   1 1 mi(Ti)      n2(T )λ  L 1    i i And the message to schedule first is the one maximizing it  For more details: Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “Message Drop and Scheduling in DTNs: Theory and Practice”, in IEEE Transactions on Mobile Computing (TMC). 11/39
  • 12. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 12/39
  • 13. Distributed version: How to calculate n and m ? n = number of copies of a message m = number of nodes that have seen the message Flood information on messages (like in RAPID by UMASS)  takes long time to converge  The information is stale by the time it reaches everyone Our solution:  Still flood information on messages  BUT, Estimate n and m at a given elapsed time from what has happened to old messages at the same elapsed time 13/39
  • 14. Distributed version (DR) Suppose m and n follow two random variables M and N Estimated delivery rate = Mean delivery rate ˆ ˆ m(T)  1 exp  λn(T)R   m(T)  E1 M(T)  1 exp  λN(T)R    M(T)       ˆ     1           i   L 1 i   L 1    L 1   L 1               We set the estimator of m to its expectation (justified by a Gaussion distribution) ˆ m(T)  m (T)  EM T         14/39
  • 15. Distributed version: Message utility expressions (H BS D)  For the delivery rate: M(T)  exp  λR N(T)    λR E 1    i i   L 1        ist 2 E L 1M(T) N(T) λ L 1 L 1 m (T)             H  For the delivery delay: or y Ba se d SD        Expectation calculated by summing over old messages 15/39
  • 16. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 16/39
  • 17. Validation Setup  DTN architecture added to the NS-2 simulator  Random Waypoint and KAIST real mobility trace  Wireless Range=100m,  CBR sources, random sources and destinations,  Each node maintains a buffer with a capacity of 20 messages Mobility model KAIST Random Waypoint Simulation duration (h): Simulated Surface (km2): 24 - 7 3*3 Number of nodes: 50 70 Average speed (m/s) : 2 - TTL (h) : 4 1 1440 360 Interval CBR (s) (10/TTL): 17/39
  • 18. Delivery Rate  HBSD outperforms existing protocols (RAPID and Epidemic based on FIFO/drop-tail) and performs close to the optimal GBSD Random Waypoint Almost 60% gain over RAPID 18/39 KAIST Traces Close by 14%
  • 19. How HBSD utilities look like ? Reduce the number of sources to 15 and decrease the CBR rate of sources from 10 to 2 messages/TTL (Low congestion regime) Schedule Youngest First Drop Oldest For a lightly loaded network, things are easier and simple policies can be applied. 19/39
  • 20. How HBSD utilities look like ? We fix the number of sources to 50 (high congestion regime) help the message over younger ones penalize – help - penalize prefer younger ones For a highly loaded network (complex function) 20/39
  • 21. Implementation / Web page And is also available for the DTN2 architecture as an external router (in C++) Code has been recently tested in the Scorpion testbed at the University of California Santa Cruz Code, papers, presentations are available at: http://planete.inria.fr/HBSD_DTN2/ 21/39
  • 22. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 22/39
  • 23. Previous context: Node Centric Node Centric vs Content Centric communications - Source: N2 - Destination: N4 1 2 N2 N5 N1 N4 2 1 1 1 N3 2 1 2 - Source: N1 - Destination: N5 23/39
  • 24. New context: Content centric Node Centric vs Content Centric communications Madonna M. Album Muse + Madonna M. Album Madonna + Muse M. Madonna M. Album Album Track – 1 Track – 2 … Track – 1 Track – 2 … Madonna M. Album Muse M. Album Track – 3 Track – 4 … Muse Madonna Muse + M. Album M. Album Track – 1 Track – 2 … Track – 1 Track – 2 … Selfish user ! [Question]: which channels and how Store Local and a node Channels much of each shouldForeign carry in !its! Make Everybody happy ? -> Store Local and Foreign Channels buffer, Selfishto maximize its future Block so as users ! (TFT) reward ?? 24/39
  • 25. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 25/39
  • 26. MobiTrade MobiTrade turns each node into a merchant fetching the content that has the highest chance to be sold to its good clients MobiTrade calculates one utility per channel that defines:  The optimal amount of storage to allocate / channel  Drop policy + Scheduling policy Node Storage Xi αi B  B  Xj * j Channel 1 Channel 2 Channel 3 Channel 4 MobiTrade approximates the Optimal U. based on the amount of exchanged content per channel @ each meeting while ensuring that selfish users are blocked  For more details: Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “MobiTrade: Trading Content in Disruption Tolerant Networks”, in proceedings of ACM CHANTS, Las Vegas, September 2011. 26/39
  • 27. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 27/39
  • 28. Collaborative experimental scenario  MobiTrade architecture added to the NS-3 simulator  Synthetic mobility model HCMM  50 users distributed into 5 groups. The simulation area is divided into a 10*10 grid of cells (5000 meters wide).  Wireless Range = 60m . Simulation Scenario Nbr. Of Users: Requested CH(s)/User: 50 2 Size of CH(s): Compare to Podcasting (PodNet project) 20 Average Delivery Rate (DR): amount of content received for channels a node requested / total amount of content generated for these channels 28/39
  • 29. How MobiTrade performs in a Collaborative scenario ? MobiTrade efficiently outperforms the two versions of Podcasting TFT causes a drop in performance among CU Drop of 6% Importance of FC(s) Almost 2x gain 29/39
  • 30. Experimental scenarios including selfish users Scenarios SS1 Nbr. Of Users: 40 CU + 10 SU 40 CU + 10SU CU: 2/20 – SU: 2/10 (SU and CU channels differ) CU, SU: 2/20 (among same channels) CU: 20 – SU: 40 Requested CH(s): Size of CH(s): SS2 CU, SU: 20 We deem such scenarios as the norm rather than the exception in the real world 30/39
  • 31. Does MobiTrade keep the system resources safe ? Enabling the TFT mechanism blocks selfish users and makes MobiTrade redispatch/reuse the saved resources among the channels shared by collaborative users SS1: CU ask for 2/20 channels and SU ask for 2/10 different channels Impact on selfish users Impact on collaborative users 31/39
  • 32. Does MobiTrade keep the system resources safe ? When TFT is used, the performance of collaborative users is not harmed, while the one of selfish users drops severely, by up to 2x for a storage of 110 contents. SS2: each user ask for 2/20 channels Impact on SU: Drop by up to 2x No Impact on CU 32/39
  • 33. Implementation / Web page MobiTrade available for the Android platform Code, papers, presentations are available at: http://planete.inria.fr/MobiTrade/  App Screenshots: 33/39
  • 34. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 34/39
  • 35. Conclusion A deep study of content sharing in DTN(s) for both:  Point-to-point communication model  Point-to-multipoint communication model New resources management policies in two versions:  Optimal one that is based on global knowledge  Practical one that efficient approximate the optimal policy GBSD/HBSD MobiTrade Validation via simulations based on synthetic mobility models and real mobility traces Implementation on real word environments (DTN2 and Android) 35/39
  • 37. Perspectives With respect to GBSD/HBSD:  Tune the utilities of our resources management policies in order to take into account different messages sizes ...  Study and design a congestion level detection mechanism to be able to switch efficiently between resources management policies … With respect to MobiTrade:  Implementing the MobiTrade protocol for other types of devices and experiment with real large scale communities of users...  Consider more complex content structures …  Study of the needed mechanisms to control possible advanced malicious attacks and behaviours that could impair MobiTrade content sharing sessions ... 37/39
  • 38. References Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “MobiTrade: Trading Content in Disruption Tolerant Networks”, in proceedings of ACM Mobicom Workshop on Challenged Networks (CHANTS), Las Vegas, September 2011. Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “Message Drop and Scheduling in DTNs: Theory and Practice”, in IEEE Transactions on Mobile Computing. Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, "Optimal Buffer Management Policy for Delay Tolerant Networks", in proceedings of the 5th IEEE Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2008), San Francisco, June 2008. (CA), June 2008. ---- BEST PAPER AWARD Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, "An Optimal Joint Scheduling and Drop Policy for Delay Tolerant Networks”, in proceedings of the WoWMoM Workshop on Autonomic and Opportunistic Communications, Newport Beach (CA), June 2008. 38/39