This document provides an outline and summary of a survey on social communication patterns and opportunistic forwarding in mobile networks. It begins with an introduction on opportunistic networking in infrastructureless environments. It then outlines key areas covered in the survey, including useful knowledge on social behavior patterns, how social patterns can impact opportunistic forwarding, and how social information can be exploited to enhance network performance. The document evaluates the state of the art in research on this topic and concludes with directions for future work, such as considering power consumption and marketing-oriented social behaviors.

1. A survey on the state-of-the-art
of social communication
patterns and opportunistic
forwarding
Emmanouil Dimogerontakis, Antonio Severien
and Faik Aras Tarhan
@RALIAS

2. Outline
● Intro
● Useful Knowledge
● Social Patterns and Opportunistic
Forwarding
● Evaluation of state of the art
● Conclusions

3. Intro
Opportunistic networking targets
infrastructureless environments where mobile
nodes wish to communicate with each other in
highly dynamic and unpredictable topology

4. Intro
Knowledge of social behaviour can be used to
enhance and fine tune performance on
opportunistic mobile networks

5. Challenges
● Increased mobility of nodes
● Mobility traces that combine traffic and social
information are rare
● Artificially generated simulation environments are not
a good replacement for real world scenarios
● Message delivery in DTNs can vary from minutes to
days
● Influence of the nature of human interactions

6. Main Approaches
Three main questions:
● Which is human social behaviour under a given
circumstance?
● Does social behaviour affect the network performance?
● How can we exploit existing social and mobility
information (social graph, contact history) to enhance
the network performance and resource usage?

7. Outline
● Intro
● Useful Knowledge
● Social Patterns and Opportunistic
Forwarding
● Evaluation of state of the art
● Conclusions

8. Social Behaviour
Community: indicate one’s social role
Centrality: reflects authority or popularity in a group
○ Degree: degree of an individual node
○ Closeness: social distance
○ Betweenness: the relay capability, or
“interpersonal influence”, of nodes
Tie-strength: the robustness of relationship for a dyad
○ Frequency
○ Recency
○ Duration
Similarity: strength of a common attribute
(Similar communication patterns)

9. Social Network Models
Ways to construct graphs with communities.
Caveman Model:
○ initially K fully connected graphs, then every edge
of the initial network is re-wired to point to a node
of another cave with a certain probability p
○ able to reproduce social structures very close to
real ones.
Kumpula Model:
○ the weights are generated dynamically and shape
the developing topology
○ local attachment, global attachment

10. Outline
● Intro
● Useful Knowledge
● Social Patterns and Opportunistic
Forwarding
● Evaluation of state of the art
● Conclusions

11. Social Patterns and Opportunistic
Forwarding
Which is human social behaviour
under a given circumstance?

12. Stumbl: Using Facebook to collect rich datasets
for opportunistic networking research
Deals with understanding the fundamental patterns of
human mobility, social relations and communications in
order to create algorithms and protocols that exploit
human mobility and consequent wireless contacts for
better dissemination.
● Unlike “Mobiclique: Middleware for mobile social
networking,” handling only one or two of the aspects
of relations, this paper focuses on all three combined

13. Stumbl: Using Facebook to collect rich datasets
for opportunistic networking research
Results:
● Social tie type has very strong impact on meeting
characteristics in terms of context, duration and
frequency of meetings
● The type of social tie has strong impact on context,
duration and frequency of meetings
● The number of Facebook communication events differs
for different relationship ties
● People communicate preferentially with friends they
also have face- to-face meetings.Thus, communication
ties are more local than social ties

14. Stumbl: Using Facebook to collect rich datasets
for opportunistic networking research
Criticism:
● Meetings and communication parts are vulnerable to
Stumbl users’ misleading information since it is self-
reporting
● Running bigger Stumbl experiments with more
participants should be the next step
● Needs to provide incentives to the users to regularly
report true data about their face-to-face meetings
● Creating a more efficient algorithm or protocol for
opportunistic networking

15. Social Patterns and Opportunistic
Forwarding
Does social behaviour affect
the network performance?

16. Dissemination in Opportunistic Mobile Ad-
hoc Networks: the Power of the Crowd
Studies fundamental properties of human interactions.
Nodes not showing up in the network frequently or
periodically might play the major role in data
dissemination depending on the characteristic of the
network
● “Bin” method observing whether people’s mobility
patterns exhibit a diurnal behavior to:
○ Classify the users as Vagabonds or Socials
Simulation:
● Metrics: Contamination
● Mobility Traces: The Dartmouth data set, The San
Francisco data set, The Second Life data set

17. Dissemination in Opportunistic Mobile Ad-
hoc Networks: the Power of the Crowd
Results:
● Vagabonds eventually dominates dissemination using
Socials if and only if
● The effectiveness of contamination is more a matter of
contact “density” in an area than an issue of social
behavior
● Vagabonds have an important role in dissemination of
information and should not be ignored unlike papers
tending to neglect this kind of users such as:
○ “PeopleRank: Social Opportunistic Forwarding”
○ “Social-Based Trust in Mobile Opportunistic
Networks”

18. Dissemination in Opportunistic Mobile Ad-
hoc Networks: the Power of the Crowd
Criticism:
● They merely focus on flooding routing:
● Message transfers are assumed to be instantaneous
● Assumption that contacts take place between any two
devices associated to the same access point is not
enough to represent the reality in fact
● Investigating the interactions between Vagabonds and
Socials in supporting information dissemination
● Investigating the dynamics of user social behavior with
respect to different social communities as done in
paper “SREP routing in opportunistic network”

19. The effect of communication pattern on
opportunistic mobile networks
How social communication patterns which are based on
basic metrics of theory of sociology affect the behaviour
of the opportunistic mobile networks.
Social patterns:
● Community-biased
● Centrality-biased (degree, closeness, betweenness)
● Tie-strength-biased
Routing algorithms with social utilities:
● Prophet (contact frequency)
● SimBet (betweenness centrality, similarity)
● FairRouting (aggregated interaction strength)

20. The effect of communication pattern on
opportunistic mobile networks
Simulation:
● Metrics: Success rate
● Mobility Traces: Reality Mining (MIT) and Haggle
(Infocom 2006)
● Community and Social information for datasets:
Constructed with community detection tool CFinder
Results:
● Social-based communication patterns increase the
system throughput of social-based routing protocols
● Tie-strength-biased offers the best performance
● Network topology can greatly influence network
performance (centrality-biased, community-biased)

21. Social-Based Trust in Mobile
Opportunistic Networks
A real-trace driven approach to study the tradeoff
between trust and success delivery rates in opportunistic
networks. Potential impact of excluding a few popular
nodes from the opportunistic forwarding can be solved by
enabling trust across communicating entities and
integrating incentives into the operation of opportunistic
networks.
Social-Based Trust Filters:
● Relay-to-Relay, Source-to-Relay
● Social Estimators: -d-distance (d is a parameter)
-Common interests
-Common Friends
-Combination

22. Social-Based Trust in Mobile
Opportunistic Networks
Simulation:
● Metrics: normalized success rate within time t,
normalized cost (i.e. # of replicas)
● Mobility Traces: CoNext07, CoNext08, Infocom06
● Community and Social information for datasets:
available from the experiment or obtained offline
Results:
● S2R filters success rate increases linearly with the cost
● R2R filters achieve better performance than S2R,
which is performing poorly
● Best R2R filter: combination 1-distance and common
friends
● The common friends technique appears to be the best
from the ones proposed

23. Selfishness, Altruism and Message
Spreading in Mobile Social Networks
Evaluate using real traces how robust an opportunistic
network is under different distributions of altruism in the
population.
Social patterns:
● Altruism Distributions: percentage of selfishness,
uniform, normal, geometric, degree-biased,
community-biased
Communication patterns:
● Uniform (evaluate with datasets)
● Community-Biased (evaluate with static social network
models)

24. Selfishness, Altruism and Message
Spreading in Mobile Social Networks
Static Social network models:
● Caveman model
● Kumpula model
Simulation:
● Metrics: delivery/success ratio
● Mobility Traces: Reality Mining (MIT) Cambridge,
Infocom05, Infocom06
● Simulator: Contact-driven
● Community and Social information for datasets: not
complete

25. Selfishness, Altruism and Message
Spreading in Mobile Social Networks
Results:
● Opportunistic networks generally be robust against
altruism
● Main cause of robustness: multiple forwarding paths
● Traffic pattern chosen for simulation has significant
impact on the social behavior impact of the simulated
network

26. Social Patterns and Opportunistic
Forwarding
How can we exploit existing social and
mobility information (social graph, contact
history) to enhance the network performance
and resource usage?

27. PeopleRank: Social Opportunistic
Forwarding
Like a distributed PageRank, PeopleRank identifies the
most popular nodes (in a social context) to forward the
message to, given that popular nodes are more likely to
meet other nodes in the networks.
Social patterns:
● People/nodes are ranked as “important” when they
are linked in a social context to many other
“important” people
● Centralized and distributed version
Routing algorithm:
● A node u forwards data to a node v that it meets if the
rank of v is higher than the rank of u.

28. PeopleRank: Social Opportunistic
Forwarding
Simulation:
● Metrics: average message delivery delay, overhead or
cost by mechanism (i.e. # of replicas)
● Mobility Traces: MobiClique, SecondLife, Infocom06
(interest,facebook,union), and Hope
● Community and Social information for datasets: some
explicit, some implicit

29. PeopleRank: Social Opportunistic
Forwarding
Results:
● forward to socially best nodes improves overall success
rate
● outperforms simple social forwarding algorithms and
some of the well-known contact-based algorithms (i.e.
Spray & Wait)
● End-to-end delay and a success rate close to those
given by flooding while reducing the number of
retransmission by 50%

30. Social relationship enhanced predictable
routing in opportunistic network
Network is composed of communities and nodes are
assumed to roam among communities somewhat regularly.
To introduce this mobility of the node, semi-deterministic
Markov process modelling is adapted and to quantify the
social degree of the node, PageRank algorithm is
introduced.
● PageRank algorithm is adapted to evaluate social
ranking of the nodes in the same community to
calculate the centrality of the nodes
● Every node in the same community has a unique social
degree

31. Social relationship enhanced predictable
routing in opportunistic network
● the total prediction correction of social degree of a
node with all communities at time t
● the average prediction correction of social degree of
node
Simulation:
Metrics: Delivery Delay, Delivery Ratio, Time To Live
(TTL), Deviation Degree
● There are several predefined communities in the
network.
● Visits are probabilistic and self-determined.
Simulator: ONE

32. Social relationship enhanced predictable
routing in opportunistic network
Results:
● The efficiency of SREP algorithms is acceptable, when
the randomness of the node deviation is lower.
● When the TTL is longer enough, the performance of
every routing improve
● SREP makes full use of the feature of human society,
and coincides the mobility of the human mobility
● SREP can yield the improvement of the delivery ratio
and reduce the delivery delay in some defined scenario

33. Forming a Social Structure in Mobile
Opportunistic Networks
They exploit the mobile nodes frequency interactions to
form social structures in opportunistic networks by
understanding the relationship between the mobile
nodes.
Methods:
○ Social Structure based on Average Frequency Interactions
■ measures how many times the same pair of nodes are
co-located and interact within a given period of time
○ Social Structure based on Periodicity Frequency
Interactions
■ based on the interactions frequency that occur in a
given period of time
○ Social Structure based on Sliding Window
■ Sliding Window (SW) is a frame that subdivided into
number of slots, which is a single time step in period

34. Forming a Social Structure in Mobile
Opportunistic Networks
Simulation:
● Metrics: In Degree and Out Degree links, Threshold
● Simulator: UCINET
Criticism:
● Mobility in the simulation is based on Random Walk. It
does take human social contact incentives into
account
○ unlike paper “Social relationship enhanced
predictable routing in opportunistic network”.

35. Forming a Social Structure in Mobile
Opportunistic Networks
Results:
● The formation of social structure is depended on the
policy of the node interactions
● A social structure of nodes is different at different
point of time
● Social Structure based on Sliding Window, is more
appropriate to be deployed as the formation of the
social structures are dynamic and represent the
current nodes interaction in which represent the
underlying current network topology

36. Bootstrapping Opportunistic
Networks Using Social Roles
Proposes Social Role Routing (SRR)
Bootstrap an opportunistic network without node contact
information from Self-Reported Social Networks (SRSN)
Avoid overloading popular nodes
Social Patterns:
● Define roles for nodes where nodes communicate in
same social classes
Routing Algorithms with social utilities:
● Social Role Routing (SRR) takes advantage of roles
grouping to make forwarding decisions

37. Bootstrapping Opportunistic
Networks Using Social Roles
Sending messages
from group A to B
- Node 1 might be
overloaded, use
node 12 and 13

38. Bootstrapping Opportunistic
Networks Using Social Roles
Routing protocol evaluation
● Epidemic: forward to any encountered node
● SimbetTS: contact history based (warm-up time)
● Social Role Routing (SRR): forward message to similar
roles
● Social Role Routing SimbetTS Hybrid: switches from
SRR to SimbetTS

39. Optimizing Message Delivery in
Mobile Opportunistic Network
Nile routing protocol
Use of replicas to increase delivery probability
Compromise between flooding and intelligent routing
techniques
- Replicate aggressively in sparse networks
- Restrict replication on dense networks
- Considers congestion control to determine replication
Social Patterns:
● Routing is flexible to adapt to different social patterns
Routing Algorithms with social utilities:
● Utilises contact frequency

40. MobiClique: Middleware for Mobile
Social Networking
Mobile social software to maintain and extend online
social networks through opportunistic encounters in real-
life
Middleware to build apps on top
- Neighborhood discovery
- User identification
- Data exchange
Social Patterns:
● Monitors mobility and social behavior
Routing Algorithms with social utilities:
● Opportunistic forwarding

41. CAMEO: Context-Aware Middleware for
Opportunistic Mobile Social Networks
Management, elaboration and dissemination of context
information
Identification of context components through hash values
Social Patterns:
● Social context
Routing Algorithms with social utilities:
● Publish/Subscribe between interest groups
● Beaconing mechanism to find relevant context
● Evaluates the probability of each neighbor node to
deliver the message to destination

42. Outline
● Intro
● Useful Knowledge
● Social Patterns and Opportunistic
Forwarding
● Evaluation of state of the art
● Conclusions

43. Evaluation
● Similar data traces - there is a need for more
experimentation
● Similar references - base knowledge from same sources
● Contradiction between papers. For example:
- [9],[4]: focus on unpopular nodes importance
- [6],[8]: focus on enhancing popular nodes
● Improvements. For example:
- [12] adds community idea in [6] with social rank

44. Outline
● Intro
● Useful Knowledge
● Social Patterns and Opportunistic
Forwarding
● Evaluation of state of the art
● Conclusions

45. Conclusions
Social aware Improvement of
Context aware opportunistic
Mobility aware forwarding
Network aware protocols

46. Future
● Power consumption related to social
behavior
● Devices are now ad hoc compatible (WiFi)
● Marketing oriented social behavior on
MANETS
● A lot of ongoing research (SOCIALNETs etc.)

47. References
1. Islam, M.A.; Waldvogel, M.; , "Optimizing message delivery in mobile-opportunistic networks," Internet
Communications (BCFIC Riga), 2011 Baltic Congress on Future , vol., no., pp.134-141, 16-18 Feb. 2011
2. Anna-Kaisa Pietilinen, Earl Oliver, Jason LeBrun, George Varghese, and Christophe Diot. 2009. MobiClique:
middleware for mobile social networking. In Proceedings of the 2nd ACM workshop on Online social networks (WOSN
'09). ACM, New York, NY, USA, 49-54.
3. Arnaboldi, V.; Conti, M.; Delmastro, F.; , "Implementation of CAMEO: A context-aware middleware for Opportunistic
Mobile Social Networks," World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 IEEE International
Symposium on a , vol., no., pp.1-3, 20-24 June 2011
4. Bigwood, G.; Henderson, T.; , "Bootstrapping opportunistic networks using social roles," World of Wireless, Mobile
and Multimedia Networks (WoWMoM), 2011 IEEE International Symposium on a , vol., no., pp.1-6, 20-24 June 2011
5. Xiaoguang Fan; Kuang Xu; Li, V.O.K.; Guang-Hua Yang; , "The effect of communication pattern on opportunistic
mobile networks," Consumer Communications and Networking Conference (CCNC), 2011 IEEE , vol., no., pp.1016-
1020, 9-12 Jan. 2011
6. Mtibaa, A.; May, M.; Diot, C.; Ammar, M.; , "PeopleRank: Social Opportunistic Forwarding," INFOCOM, 2010
Proceedings IEEE , vol., no., pp.1-5, 14-19 March 2010
7. Pan Hui; Kuang Xu; Li, V.O.K.; Crowcroft, J.; Latora, V.; Lio, P.; , "Selfishness, Altruism and Message Spreading in
Mobile Social Networks," INFOCOM Workshops 2009, IEEE , vol., no., pp.1-6, 19-25 April 2009
8. Mtibaa, A.; Harras, K.A.; , "Social-Based Trust in Mobile Opportunistic Networks," Computer Communications and
Networks (ICCCN), 2011 Proceedings of 20th International Conference on , vol., no., pp.1-6, July 31 2011-Aug. 4 2011
9. Zyba, G.; Voelker, G.M.; Ioannidis, S.; Diot, C.; , "Dissemination in opportunistic mobile ad-hoc networks: The power
of the crowd," INFOCOM, 2011 Proceedings IEEE , vol., no., pp.1179-1187, 10-15 April 2011
10. Lenando, H.; Zen, K.; Jambli, M.N.; Thangaveloo, R.; , "Forming a Social structure in mobile opportunistic
networks," Communications (APCC), 2011 17th Asia-Pacific Conference on , vol., no., pp.450-455, 2-5 Oct. 2011

48. References
11. Hossmann, T.; Legendre, F.; Nomikos, G.; Spyropoulos, T.; , "Stumbl: Using Facebook to collect rich datasets for
opportunistic networking research," World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 IEEE
International Symposium on a , vol., no., pp.1-6, 20-24 June 2011
12. Xie, X., Zhang, Y., Dai, C., & Song, M. (2011). Social Relationship Enhanced Predicable Routing in Opportunistic
Network. 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks, 268-275.
13. http://www.haggleproject.org/
14. http://reality.media.mit.edu/
15. http://www.social-nets.eu/
16. http://crawdad.cs.dartmouth.edu/
17. S. Wasserman and K. Faust, Social network analysis: methods and applications, Cambridge University Press, 1994
18. J. M. Kumpula, J. P. Onnela, J. Saramaki, K. Kaski, and J. Kertesz. Emergence of communities in weighted
networks. 2007
19. D. J. Watts. Small Worlds The Dynamics of Networks between Order and Randomness. Princeton Studies on
Complexity. Princeton University Press, 1999

49. A survey on the state-of-the-art
of social communication
patterns and opportunistic
forwarding
Emmanouil Dimogerontakis, Antonio Severien
and Faik Aras Tarhan
@RALIAS