A boring presentation about social mobile communication patterns and opportunistic forwarding

A survey on the state-of-the-art of social communication

patterns and opportunistic forwarding

Emmanouil Dimogerontakis, Antonio Severien and Faik Aras Tarhan

@RALIAS

Outline

● Intro● Useful Knowledge● Social Patterns and Opportunistic

Forwarding● Evaluation of state of the art● Conclusions

Intro

Opportunistic networking targets infrastructureless environments where mobile nodes wish to communicate with each other in

highly dynamic and unpredictable topology

Intro

Knowledge of social behaviour can be used to enhance and fine tune performance on

opportunistic mobile networks

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

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?

Outline



Social Behaviour

Community: indicate one’s social roleCentrality: reflects authority or popularity in a group

○ Degree: degree of an individual node ○ Closeness: social distance○ Betweenness: the relay capability, or

“interpersonal influence”, of nodesTie-strength: the robustness of relationship for a dyad

○ Frequency○ Recency○ Duration

Similarity: strength of a common attribute(Similar communication patterns)

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

Outline



Social Patterns and Opportunistic Forwarding

Which is human social behaviour under a given circumstance?

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


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


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


Does social behaviour affect the network performance?

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


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”


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”

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-biasedRouting algorithms with social utilities:● Prophet (contact frequency)● SimBet (betweenness centrality, similarity)● FairRouting (aggregated interaction strength)

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 CFinderResults:● 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)

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

Social-Based Trust in Mobile Opportunistic NetworksSimulation:● 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 offlineResults:● 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

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)


Static Social network models:● Caveman model● Kumpula modelSimulation:● Metrics: delivery/success ratio● Mobility Traces: Reality Mining (MIT) Cambridge,

Infocom05, Infocom06● Simulator: Contact-driven● Community and Social information for datasets: not

complete


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


How can we exploit existing social and mobility information (social graph, contact

history) to enhance the network performance and resource usage?

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 versionRouting 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.


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


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%

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


● 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


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

Forming a Social Structure in Mobile Opportunistic NetworksThey 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

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”.

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

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


Sending messages from group A to B - Node 1 might be overloaded, use node 12 and 13


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

Optimizing Message Delivery in Mobile Opportunistic Network

Nile routing protocolUse of replicas to increase delivery probabilityCompromise 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 patternsRouting Algorithms with social utilities:

● Utilises contact frequency

MobiClique: Middleware for Mobile Social Networking

Mobile social software to maintain and extend online social networks through opportunistic encounters in real-lifeMiddleware to build apps on top

- Neighborhood discovery- User identification- Data exchange

Social Patterns:

● Monitors mobility and social behaviorRouting Algorithms with social utilities:

● Opportunistic forwarding

CAMEO: Context-Aware Middleware for Opportunistic Mobile Social Networks

Management, elaboration and dissemination of context informationIdentification 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

Outline



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

Outline



Conclusions

Social awareContext awareMobility awareNetwork aware

Improvement of opportunistic forwarding protocols

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.)

References1. 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. 20112. 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 20114. 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 20115. 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. 20116. Mtibaa, A.; May, M.; Diot, C.; Ammar, M.; , "PeopleRank: Social Opportunistic Forwarding," INFOCOM, 2010 Proceedings IEEE , vol., no., pp.1-5, 14-19 March 20107. 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 20098. 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 20119. 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 201110. 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

References11. 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 201112. 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, 199418. J. M. Kumpula, J. P. Onnela, J. Saramaki, K. Kaski, and J. Kertesz. Emergence of communities in weighted networks. 200719. D. J. Watts. Small Worlds The Dynamics of Networks between Order and Randomness. Princeton Studies on Complexity. Princeton University Press, 1999

http://www.haggleproject.org/

http://reality.media.mit.edu/

http://www.social-nets.eu/

http://crawdad.cs.dartmouth.edu/

A survey on the state-of-the-art of social communication

patterns and opportunistic forwarding

Emmanouil Dimogerontakis, Antonio Severien and Faik Aras Tarhan

@RALIAS

A boring presentation about social mobile communication patterns and opportunistic forwarding

Technology

Transcript of A boring presentation about social mobile communication patterns and opportunistic forwarding