Content Dissemination in Mobile Social Networks Cheng-Fu Chou.
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Transcript of Content Dissemination in Mobile Social Networks Cheng-Fu Chou.
![Page 1: Content Dissemination in Mobile Social Networks Cheng-Fu Chou.](https://reader036.fdocuments.us/reader036/viewer/2022062422/56649f265503460f94c3d68f/html5/thumbnails/1.jpg)
Content Dissemination in Mobile Social Networks
Cheng-Fu Chou
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Content Dissemination in Mobile Social Networks• Users intrinsically form a mobile social network – Ubiquitous mobile devices, e.g., smart phone– Proximity-based sharing capability, e.g., WiFi, or
bluetooth
1. Opportunistically distribute content objects
2. Offload 3G/4G traffic
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Delay Tolerant Networks• DTN: – No network infrastructures– intermitted network connections– Unpredictable node mobility
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Unicast in DTN• Unicast routing – Constraint: buffer size, hop count, …
• Existing works– Probability-based forwarding
• Delivery probability• A. Lindgren, A. Doria, et al. "Probabilistic routing in
intermittently connected networks," In Proc. SAPIR, 2004.
– Social-based forwarding• Social properties, such as centrality and communities• E.M. Daly, M, Haahr, “Social network analysis for routing
in disconnected delay-tolerant MANETs,” In ACM MobiHoc,2007
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Multicast in DTN• Multicast routing– Delivering to a set of given destinations– Goal: minimize delay, maximize delivery rate• W. Gao, Q. Li, et al. “Multicasting in Delay Tolerant
Networks, A Social Network Perspective,” In ACM MobiHoc,2009
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Content Dissemination• Content Dissemination– No specific destinations • e.g., information broadcasting, content (audio/video)
publishing
– Distribute content to as many users as possible • Cellular Traffic Offloading [Bo Han et al. , CHANTS’10]
– Offload cellular traffic through opportunistic communication
– Focus on cellular communication target set selection
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Ours• DIFFUSE [TVT’11]– Single content diffusion in MSNs
• Ad propagation or audio/video content dissemination
– Different from related work• No specific destinations• Forward to as many users as possible• Transmission time is non-neglected
– Unicast
• PrefCast [Infocom’12]– Multi-content disseminations in a MSN – Satisfying all users’ preference as much as possible– Focusing on the content broadcasting strategy
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DIFFUSE
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Motivation
those users who have high contact frequency may belong to the same community
User contribution: The number of useful contacts that the user can encounter after it becomes a forwarder
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Idea• Due to the limitation of the transmission time,
nodes should take both contact time and contribution into account
• Challenge: – Contribution– Contact duration
Alice
Bob
CarolDaniel
Carol
Contribution 1.3
Duration 1
Bob
Contribution 0.5
Duration 2
Daniel
Contribution 1.8
Duration 2
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Problem Definition and Assumptions
• One source disseminates a single message• Relay node that can help propagate copy to those
who have not received the message• Discrete model with the time-slot size Ttx
(transmission time)• A user can only forward the message to a single
contact at a timeGoal: Distribute the message to as many users as possible before the deadline Tmax expires
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Motivating Example 1• Contact users with different contact duration
→ A B C
Contact duration(relay, receivers)
Relay node
Candidate receivers
A
B
CCBA
Select the receivers that have the shortest contact duration first
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Motivating Example 2• Contact users with the same contact duration,
yet different contributions
→ A B C
Relay node
Candidate receivers
A
B
C
Contribution:
A: 1.2
B: 0.9
C: 0.5 CBA
Select the receivers that have the largest contribution first
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Motivating Example 3• Contact users with different contact durations
and contributions
→ C B A
Relay node
Candidate receivers
A
B
C
Contribution:
A: 1.3
B: 0.9
C: 0.5
C B AA B
C X
Take both contact duration and contribution into account
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Forwarding Scheduling Problem
• Backward induction algorithm– Run in pseudo-polynomial time O(δ|Gi|)
Subject to:
Whether user j can download the message at time t
dij
ts te
contribution = 0
j j
t
jcontribution =
Contribution of user j at time t
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Backward Induction Algorithm
E A C X X B
→ E A C B
16
Relay node
Candidate receivers Contribution:A: 0.5
B: 0.2
C: 0.7
D: 0.2
E: 0.4
A
B
C
D
EBCAE
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Estimate of contribution
• Duration between t and Tmax
• How many users that do not own object m have contacts with user B between (t,Tmax)
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Estimate of contact duration• Motivation: Average contact duration is too rough• The duration of a contact is correlated to the event
that they join• Characterize each event g by a vector : = <b1, b2,…,bk,…>• Similarity between two events g and g’– Hamming distance between and
vg
vg 'gv
V1 = <01100>
V2 = <01001>Similarity 12 = -2
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Estimate of contact duration• Contacts in two events are more likely to have
the same duration if these events are composed of the same subset of users
• Cluster-based estimation
New event
dij = ∑dij(g) / |C2|
Average duration between i and j in events belong to cluster C2
C2
C1
C3
History events that include i and j
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Performance
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Performance Evaluation
• Experiment Setting– Real trace from class schedule of University of
Singapore– Bluetooth with the throughput 128kbps– One randomly selected source that transmits a file
with the size 30MB• Evaluation– Accuracy of contribution and contact duration
estimation– Performance of DIFFUSE
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Accuracy of Contribution Estimation
0 5 10 15 20 25 30 35 40 450
5
10
15
20
25
30
35
40
45
ranking of estimated contribution
rank
ing
of a
ccur
ate
cont
ributi
on
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Accuracy of Duration Estimation• CDF of Estimation Error
31%
49%74%
84%
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Comparison schemes• Oracle– Contribution: number of users that have not got the copy
in the system– Exact contact duration
• Epidemic– each relay node randomly selects a contact as the
receiver at each time-slot– A. Vahdat and D. Becker, “Epidemic Routing for Partially Connected Ad Hoc
Networks,” Technical Report CS-200006, Duke University, Tech. Rep., 2000.
• PROPHET– estimate the probability of contact between a relay and
the destination– A. Lindgren, A. Doria, et al. Probabilistic routing in intermittently connected networks. In
Proc. SAPIR, 2004.
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Receive nodes vs. Deadline
0 1/7 2/7 3/7 4/7 5/7 6/7 10
200
400
600
800
1000
1200
1400
1600
1800
OracleDIFUUSEPROPHETEpidemic
deadline (day)
# re
ceiv
e no
des
improve 145%
coverage
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Histogram of contribution of each user
1 2 3 4 5 6 7 8 9 100
0.5
1
1.5
2
2.5
3
3.5
OracleDIFUUSEPROPHETEpidemic
# contribution nodes of one source
# no
des(
log1
0)
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Receive nodes vs. File size
101%
185%
3%25%
10 20 30 400
0.5
1
1.5
2
2.5
3
3.5
4
OracleDIFFUSEPROPHETEpidemic
file size (MB)
# re
ceiv
e n
odes
(log1
0)
It becomes more important to select receivers when transmission time becomes long because only few contacts can get the copy
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Percentage of the groups with relay node
10 20 30 400
5
10
15
20
25
OracleDIFFUSEPROPHETEpidemic
file size (MB)
% g
roup
s with
rela
y no
de
Our scheme can disseminate the copy to more different groups
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Conclusions• Propose a backward induction algorithm for
content diffusion in MSNs• Consider the impact of contribution and
contact duration, and provide prediction metrics
• Achieve better delivery ratio than Epidemic and PROPHET, even close to the solution with oracle information
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PrefCast
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Existing Dissemination ProtocolsSpeed up content dissemination
PrefCastA content dissemination protocol that
maximally satisfies user preference
without considering heterogeneous user preferences for various content objects
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A Naïve Solution
• Broadcast the object that maximizes the utility of local contacts– Suboptimal: Neglect the impact of future contacts
(u1,u2)=(10,5)
(5,3)
(3,10)
(5,8)
(3,8)
A (2,10)
B
GA
GB
A
B
F
u1 u2
A 10 5
B 5 3
Total 15 8
u1 u2
A + GA 20 33
B + GB 8 11
Total 28 44
Local contribution
Globalcontribution
Say the contact duration only allows F to broadcast 1 object
To maximize local utility, the forwadrer should broadcast object 1
To maximize global utility, the forwarder should broadcast object 2
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Our Goal
• Take future contribution into account– How to predict future contribution?
• Broadcast the objects of interest within limited contact duration– Given future contribution estimation, how to find
the optimal forwarding schedule
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1. How to Predict Future Contribution?
• How many future contacts can be encountered by its current contact
• How to know the preference of those future contacts?
A
(3,10)
(5,8)(2,10)A
GA
??
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2. How to Find the Forwarding Schedule?
• Each contact has a different contact duration
A
B
F
C E
D
timeABCDE
Transmission time of one object
Intuitively, should give a contact with a short contact duration a higher priority
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• Take future contribution into account– How to predict future contribution?– Utility contribution estimation
• Broadcast the objects of interest within limited contact duration– Given future contribution estimation, how to find
the optimal forwarding schedule– Optimal forwarding scheduling algorithm
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Maximum-Utility Forwarding Model When a forwarder f encounters a group of contacts M in a set of available time-slots T
Determine a forwarding schedule xm,t that maximizes preference contribution
Subject to
Global contribution of forwarding object m at time t
Single item per time slot
Broadcast once per object
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Maximal Weight Bipartite Matching
• Constraint 1: Each time-slot can only connect to an object• Constraint 2: Each object can only be assigned one time-slot• Any bipartite matching is a feasible solution• The total utility contribution equals the weight of the matching• Maximum utility = Maximal weight bipartite matching
– Solved by the Hungarian algorithm [Kuhn-NRLQ’55]
m1 m2 m3 m4
t1 t2 t3
Objects
Time-slots
ωgm4,t3
ωgm1,t1
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• Take future contribution into account– How to predict future contribution?– Utility contribution estimation
• Broadcast the objects of interest within limited contact duration– Given future contribution estimation, how to find
the optimal forwarding schedule– Optimal forwarding scheduling algorithm
ωgm,t
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Estimating Global Utility Contribution
timeABCDE
Vτ = {A, B, C, D, E}
A already has object mC and D leave before time-slot t
U(E,m,t)
U(B,m,t)
Future contribution that i can generate if it gets object m at time t
wgm,t=U(B,m,t) +U(E,m,t)
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Estimating Future Utility Contribution
• Future contribution: U(i,m,t)
– Duration between t and Texpire
– How may users that do not own object m have contacts with user B between (t,Texpire)
– Preference of user B’s contacts for object m
timeB U(B,m,t)
t Texpire
Contribute object m to other users between (t,Texpire)
Computed by neighbor B Forwarder makes decision in a distributed manner
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Performance
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Simulation Settings• Traces
• User preference profile– Last.fm– 8,000 users – 100 favorite songs– Classify by singers
NUS Infocom MIT SLAW (synthetic model)
No. of users 500/22341 78 97 500
Duration 77(hr) 16(hr) 35 (days) 10(hr)
SingersAcen 1
Adriana Evans 3
Air 5
Bit Shifter 6
Caro Emerald 2
… …
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Cumulative Utility
5 15 25 35 45 55 65 7502468
10121416
Time-slot (hours)
Aver
age
utilit
y
(a) NUS (b) infocom
(c) MIT (d) SLAW
- PrefCast- Local Utility- Epidemic Routing
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Cumulative Utility
5 15 25 35 45 55 65 7502468
10121416 PrefCast
Local UtilityEpidemic Routing
Time-slot (hours)
Aver
age
utilit
y
2 4 6 8 10 12 14 160
5
10
15
20
PrefCast Local UtilityEpidemic Routing
Time-slot (hours)
Aver
age
utilit
y
5 10 15 20 25 30 350
5
10
15
20
25 PrefCast Local UtilityEpidemic Routing
Time-slot (days)
Aver
age
utilit
y
1 2 3 4 5 6 7 8 9 100
5
10
15
20
25 PrefCast Local UtilityEpidemic Routing
Time-slot (hours)
Aver
age
utilit
y
Improve the average utility by ~25%
(a) NUS (b) infocom
(c) MIT (d) SLAW
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Impact of Number of Users
1 2 3 4 5 6 7 8 9 100
5
10
15
20
25
30
35
time slot (hours)
aver
age
utilit
y
SLAW
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Impact of Number of Users
1 2 3 4 5 6 7 8 9 100
5
10
15
20
25
30
35 PrefCast (250 users)Local Utility (250 users)PrefCast (200 users)Local Utility (200 users)PrefCast (150 users)Local Utility (150 users)
time slot (hours)
aver
age
utilit
y
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Impact of Number of Users
1 2 3 4 5 6 7 8 9 100
5
10
15
20
25
30
35 PrefCast (250 users)Local Utility (250 users)PrefCast (200 users)Local Utility (200 users)PrefCast (150 users)Local Utility (150 users)
time slot (hours)
aver
age
utilit
y
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Impact of Number of Users
1 2 3 4 5 6 7 8 9 100
5
10
15
20
25
30
35 PrefCast (250 users)Local Utility (250 users)PrefCast (200 users)Local Utility (200 users)PrefCast (150 users)Local Utility (150 users)
time slot (hours)
aver
age
utilit
y
The utility improvement increases when there are fewer users helping disseminate the object
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Conclusions
• PrefCast: Distributed preference-aware content dissemination protocol for mobile social networks– Optimal forwarding scheduling model– Prediction of the future contributions
• Shown utility improvement via real traces and synthetic traces
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Thank You