A boring presentation about social mobile communication patterns and opportunistic forwarding

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A boring presentation about social mobile communication patterns and opportunistic forwarding

  1. 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. 2. Outline  ● Intro● Useful Knowledge● Social Patterns and Opportunistic Forwarding● Evaluation of state of the art● Conclusions
  3. 3. Intro     Opportunistic networking targetsinfrastructureless environments where mobilenodes wish to communicate with each other in highly dynamic and unpredictable topology  
  4. 4. IntroKnowledge of social behaviour can be used to enhance and fine tune performance on opportunistic mobile networks      
  5. 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. 6. Main ApproachesThree 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. 7. Outline  ● Intro● Useful Knowledge● Social Patterns and Opportunistic Forwarding● Evaluation of state of the art● Conclusions
  8. 8. Social BehaviourCommunity: 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 ○ DurationSimilarity: strength of a common attribute(Similar communication patterns)
  9. 9. Social Network ModelsWays 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. 10. Outline  ● Intro● Useful Knowledge● Social Patterns and Opportunistic Forwarding● Evaluation of state of the art● Conclusions
  11. 11. Social Patterns and OpportunisticForwarding    Which is human social behaviour under a given circumstance?
  12. 12. Stumbl: Using Facebook to collect rich datasetsfor opportunistic networking researchDeals with understanding the fundamental patterns ofhuman mobility, social relations and communications inorder to create algorithms and protocols that exploithuman mobility and consequent wireless contacts forbetter 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. 13. Stumbl: Using Facebook to collect rich datasetsfor opportunistic networking researchResults:● 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. 14. Stumbl: Using Facebook to collect rich datasetsfor opportunistic networking researchCriticism:● 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. 15. Social Patterns and OpportunisticForwarding    Does social behaviour affect the network performance?  
  16. 16. Dissemination in Opportunistic Mobile Ad-hoc Networks: the Power of the CrowdStudies fundamental properties of human interactions.Nodes not showing up in the network frequently orperiodically might play the major role in datadissemination depending on the characteristic of thenetwork● “Bin” method observing whether people’s mobility patterns exhibit a diurnal behavior to: ○ Classify the users as Vagabonds or SocialsSimulation:● Metrics: Contamination● Mobility Traces: The Dartmouth data set, The San Francisco data set, The Second Life data set 
  17. 17. Dissemination in Opportunistic Mobile Ad-hoc Networks: the Power of the CrowdResults:● 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. 18. Dissemination in Opportunistic Mobile Ad-hoc Networks: the Power of the CrowdCriticism:● 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. 19. The effect of communication pattern onopportunistic mobile networksHow social communication patterns which are based onbasic metrics of theory of sociology affect the behaviourof 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)
  20. 20. The effect of communication pattern onopportunistic mobile networksSimulation:● 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)
  21. 21. Social-Based Trust in MobileOpportunistic NetworksA real-trace driven approach to study the tradeoffbetween trust and success delivery rates in opportunisticnetworks. Potential impact of excluding a few popularnodes from the opportunistic forwarding can be solved byenabling trust across communicating entities andintegrating incentives into the operation of opportunisticnetworks.Social-Based Trust Filters: ● Relay-to-Relay, Source-to-Relay ● Social Estimators: -d-distance (d is a parameter) -Common interests -Common Friends -Combination
  22. 22. Social-Based Trust in MobileOpportunistic 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
  23. 23. Selfishness, Altruism and MessageSpreading in Mobile Social NetworksEvaluate using real traces how robust an opportunisticnetwork is under different distributions of altruism in thepopulation. Social patterns:● Altruism Distributions: percentage of selfishness, uniform, normal, geometric, degree-biased, community-biasedCommunication patterns:● Uniform (evaluate with datasets)● Community-Biased (evaluate with static social network models) 
  24. 24. Selfishness, Altruism and MessageSpreading in Mobile Social NetworksStatic 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 
  25. 25. Selfishness, Altruism and MessageSpreading 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. 26. Social Patterns and OpportunisticForwarding    How can we exploit existing social and mobility information (social graph, contact history) to enhance the network performance and resource usage? 
  27. 27. PeopleRank: Social OpportunisticForwardingLike a distributed PageRank, PeopleRank identifies themost popular nodes (in a social context) to forward themessage to, given that popular nodes are more likely tomeet 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.
  28. 28. PeopleRank: Social OpportunisticForwarding 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. 29. PeopleRank: Social OpportunisticForwarding 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. 30. Social relationship enhanced predictablerouting in opportunistic networkNetwork is composed of communities and nodes areassumed to roam among communities somewhat regularly.To introduce this mobility of the node, semi-deterministicMarkov process modelling is adapted and to quantify thesocial degree of the node, PageRank algorithm isintroduced.● 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. 31. Social relationship enhanced predictablerouting 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 nodeSimulation: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. 32. Social relationship enhanced predictable routing in opportunistic networkResults:● 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. 33. Forming a Social Structure in MobileOpportunistic NetworksThey exploit the mobile nodes frequency interactions toform social structures in opportunistic networks byunderstanding the relationship between the mobilenodes.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. 34. Forming a Social Structure in MobileOpportunistic NetworksSimulation:● 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. 35. Forming a Social Structure in MobileOpportunistic NetworksResults:● 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. 36. Bootstrapping OpportunisticNetworks Using Social RolesProposes Social Role Routing (SRR)Bootstrap an opportunistic network without node contactinformation from Self-Reported Social Networks (SRSN)Avoid overloading popular nodes Social Patterns:● Define roles for nodes where nodes communicate in same social classesRouting Algorithms with social utilities:● Social Role Routing (SRR) takes advantage of roles grouping to make forwarding decisions
  37. 37. Bootstrapping OpportunisticNetworks Using Social RolesSending messagesfrom group A to B - Node 1 might beoverloaded, usenode 12 and 13
  38. 38. Bootstrapping OpportunisticNetworks Using Social RolesRouting 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. 39. Optimizing Message Delivery inMobile Opportunistic NetworkNile routing protocolUse of replicas to increase delivery probabilityCompromise between flooding and intelligent routingtechniques - Replicate aggressively in sparse networks - Restrict replication on dense networks - Considers congestion control to determine replicationSocial Patterns:● Routing is flexible to adapt to different social patternsRouting Algorithms with social utilities:● Utilises contact frequency
  40. 40. MobiClique: Middleware for MobileSocial NetworkingMobile social software to maintain and extend onlinesocial networks through opportunistic encounters in real-lifeMiddleware to build apps on top - Neighborhood discovery - User identification - Data exchangeSocial Patterns:● Monitors mobility and social behaviorRouting Algorithms with social utilities:● Opportunistic forwarding
  41. 41. CAMEO: Context-Aware Middleware forOpportunistic Mobile Social NetworksManagement, elaboration and dissemination of contextinformationIdentification of context components through hash values Social Patterns:● Social contextRouting 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. 42. Outline  ● Intro● Useful Knowledge● Social Patterns and Opportunistic Forwarding● Evaluation of state of the art● Conclusions
  43. 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. 44. Outline  ● Intro● Useful Knowledge● Social Patterns and Opportunistic Forwarding● Evaluation of state of the art● Conclusions
  45. 45. ConclusionsSocial aware Improvement ofContext aware opportunisticMobility aware forwardingNetwork aware protocols  
  46. 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. 47. References1. Islam, M.A.; Waldvogel, M.; , "Optimizing message delivery in mobile-opportunistic networks," InternetCommunications (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 (WOSN09). ACM, New York, NY, USA, 49-54.3. Arnaboldi, V.; Conti, M.; Delmastro, F.; , "Implementation of CAMEO: A context-aware middleware for OpportunisticMobile Social Networks," World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 IEEE InternationalSymposium on a , vol., no., pp.1-3, 20-24 June 20114. Bigwood, G.; Henderson, T.; , "Bootstrapping opportunistic networks using social roles," World of Wireless, Mobileand 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 opportunisticmobile 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, 2010Proceedings 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 inMobile 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 andNetworks (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 powerof 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 opportunisticnetworks," Communications (APCC), 2011 17th Asia-Pacific Conference on , vol., no., pp.450-455, 2-5 Oct. 2011 
  48. 48. References11. Hossmann, T.; Legendre, F.; Nomikos, G.; Spyropoulos, T.; , "Stumbl: Using Facebook to collect rich datasets foropportunistic networking research," World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 IEEEInternational 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 OpportunisticNetwork. 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 weightednetworks. 200719. D. J. Watts. Small Worlds The Dynamics of Networks between Order and Randomness. Princeton Studies onComplexity. Princeton University Press, 1999    
  49. 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

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