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Using Social Information to
Improve Opportunistic Networking


                    Waldir Moreira
                waldir.junior@ulusofona.pt
                         Feb. 1st, 2012
                    SITI Brainstorm Meeting
Agenda

• Introduction
• Application Environments
• Routing over Opportunistic Networks
• Our Work
• Conclusions and Future Work




                                        2
Introduction




               3
Picture today

• Users are eager for retrieving/providing
 information
• Popularization of portable devices




                                             4
Straightforward Definition


OppNets are highly dynamic, composed of
mobile and static nodes (i.e., devices) and
take advantages of opportunistic time-
varying contacts among users carrying them
to exchange information




                                          5
General OppNets
Characteristics

• Occasional contacts
• Intermittent connectivity
• Highly mobile and fixed nodes
• Power-constrained devices
• Possible nonexistence of e2e paths



                                       6
Application
Environments



               7
Different Environments

• Disruptive environments:
- Sparse scenarios where communication
 is established through sporadic contacts
• Urban environments
- Dense scenarios with communication
 suffering different interference levels



                                            8
Disruptive Environments
Deep Space Communications

• Purpose: provide communication means
 for manned/robotic exploration
• Main challenges: very long delays,
 sparseness, shadow areas and spacecraft
 lifetime
• Function: Information and commands are
 exchanged between landers/rovers and
 earth station through orbiters

                                           9
Disruptive Environments
Deep Space Communications




[1] News on Deep Space Networking
[2] Mars Reconnaissance Orbiter


                                    10
Disruptive Environments
Networks for Developing World

• Purpose: provide asynchronous Internet
 access despite the scarce/expensive
 infrastructure
• Main challenges: long delays and
 scarce/expensive infrastructure
• Function: data is sent/retrieved either
 through USB stick carried by a motorbiker
 or via dial-up connection

                                           11
Disruptive Environments
Networks for Developing World




[3] S. Jain, K. Fall, R. Patra, Routing in a delay tolerant network, 2004
[4] News on Pigeon Carrier


                                                                            12
Disruptive Environments
Zebranet

• Purpose: Study zebra movements
 through collars carried by them
• Main challenges: energy constraints
• Function: collars opportunistically
 exchange GPS location later then
 obtained by scientists



                                        13
Disruptive Environments
Zebranet




                          14
Disruptive Environments
Tactical Military Networks

• Purpose: establish quick communication
 means among military soldiers, vehicles,
 and aircrafts
• Main challenges: high disruption and
 partition
• Function: information is relayed among
 military units


                                            15
Disruptive Environments
Tactical Military Networks




[5] MITRE Corporation
  (C2 On-the-Move Network, Digital Over-the-Horizon Relay)


                                                             16
Urban Environments
Opportunistic Sensing

• Purpose: gather information from sensing
 systems
• Main challenges: short contact times
• Function: sensor present in different
 devices gather information which is then
 collected mobile devices (i.e., custodian)
 to be transfered to the sensing system
 central

                                              17
Urban Environments
Opportunistic Sensing




[6] CamMobSens - Cambridge University Pollution Monitoring Initiative



                                                                        18
Routing over
Opportunistic Networks



                         19
What is it about?


Considers any contact among nodes and
forwarding decisions are made using locally
collected knowledge about node behavior to
predict which nodes are likely to deliver a
content or bring it closer to the destination




                                            20
2000-2010 Analysis




[7] W. Moreira and P. Mendes, “Survey on opportunistic routing for delay
tolerant networks,” SITI, University Lusofona, February, 2011


                                                                           21
Existing Taxonomies




[7]


                      22
Major Routing Families




[7] W. Moreira and P. Mendes, “Survey on opportunistic routing for delay
tolerant networks,” SITI, University Lusofona, February, 2011


                                                                           23
Social Aspects:
The New Trend

• Since 2007
• Have shown great potential
• Use social relationship
• Much wiser decisions




                               24
Replication-based Approaches
Social Similarity
• Community Detection: creation of communities
 considering people social relationships
 - Bubble Rap
   * Forwarding based on
   community and local/
   global centrality



[8] P. Hui, J. Crowcroft, E. Yoneki, BUBBLE Rap: Social-based Forwarding in
Delay Tolerant Networks, 2011


                                                                          25
Replication-based Approaches
Social Similarity
• Shared Interests: nodes with the same interest
 as destination are good forwarders
- SocialCast
   * predicted node’s co-location (probability of
  nodes being co-located with others)
   * change in degree of connectivity (mobility and
  changes in neighbor sets)

[9] P. Costa, C. Mascolo, M. Musolesi, G. P. Picco, Socially-aware routing for
publish-subscribe in delay-tolerant mobile ad hoc networks, 2008


                                                                            26
Replication-based Approaches
Social Similarity

• Node Popularity: use of social information
 to generate ranks to nodes based on their
 position on a social graph
 - PeopleRank
   * Forwarding based on social ranking of
  nodes

[10] A. Mtibaa, M. May, M. Ammar, C. Diot, Peoplerank: Combining social
and contact information for opportunistic forwarding, 2010


                                                                          27
Our Work




           28
Motivation

• Community detection, shared interests, node popularity

• Communities are statically defined

• Do not consider the age of contacts when computing the
  centrality

• Strong assumptions

• Full knowledge on social information is not enough

• Some social metrics (e.g., betweenness centrality) can lead to
  node homogeneity

[11] T. Hossmann, T. Spyropoulos, F. Legendre, Know thy neighbor:
Towards optimal mapping of contacts to social graphs for dtn routing, 2010


                                                                         29
Our Proposal

     dLife
 
     People's daily life routine and their social ties
     to reach a clean representation of social
     interactions

     Time-Evolving Contact Duration (TECD)
 
     Weights social interactions based on statistical
     contact duration nodes have over time

     TECD Importance (TECDi)
 
     Estimates the importance of nodes
                                                         30
Our Proposal




               31
Promising results
              Average Delivery Probability
                   Created Scenario

1.0

0.8

0.6

0.4

0.2
                                                                       Average Delivery Probability
0.0                                                                             Traces
      1 day   2 day         4 day      1 week   3 week
                           TTL                           1.0

                                                         0.8

                                                         0.6
              BubbleRap
              dLife Comm                                 0.4
              dLife
                                                         0.2

                                                         0.0
                                                               1 day   2 day        4 day       1 week   3 week
                                                                                   TTL




                                                                                                                  32
Promising results
                                  Average Cost
                                 Created Scenario

                1600
                1400
                1200
                1000
# of replicas




                800
                600
                400
                200                                                                                    Average Cost
                  0                                                                                       Traces
                       1 day    2 day       4 day   1 week   3 week
                                         TTL                                          40
                                                                                      35
                                                                                      30
                                                                                      25
                                                                      # of replicas   20
                               BubbleRap
                               dLife Comm                                             15
                               dLife                                                  10
                                                                                      5
                                                                                      0
                                                                                           1 day   2 day      4 day   1 week   3 week
                                                                                                             TTL




                                                                                                                                        33
Promising results
                           Average Latency
                           Created Scenario

          45000
          40000
          35000
          30000
Seconds




          25000
          20000
          15000
          10000                                                                            Average Latency
          5000                                                                                 Traces
              0
                  1 day    2 day       4 day   1 week   3 week             45000
                                   TTL                                     40000
                                                                           35000
                                                                           30000

                                                                 Seconds   25000
                          BubbleRap
                                                                           20000
                          dLife Comm
                          dLife                                            15000
                                                                           10000
                                                                            5000
                                                                               0
                                                                                   1 day   2 day     4 day   1 week   3 week
                                                                                                   TTL




                                                                                                                               34
Conclusions and Future Work

        Functions in separate had good overall performance

        Their combination sure provided improvements

        dLife is able to transcribe the dynamic behavior
        found on users' interactions into clean social
        representations

        Plans
    
        Improve it by introducing randomness and a stale-
        data removal scheme




                                                            35
References
[1] News on Deep Space Networking -
   http://www.engadget.com/2008/11/19/nasas-interplanetary-internet-tests-a-
   success-vint-cerf-triump/

[2] Mars Reconnaissance Orbiter -
   http://www.nasa.gov/mission_pages/MRO/news/mro-20060912.html

[3] S. Jain, K. Fall, R. Patra, Routing in a delay tolerant network, in: Proceedings of
   the ACM SIGCOMM, Portland, USA, August, 2004.

[4] News on Pigeon Carrier - http://www.dailymail.co.uk/news/article-
   1212333/Pigeon-post-faster-South-Africas-Telkom.html

[5] MITRE Corporation (US Marine Corps) (Presentation on C2 On-the-Move
   Network, Digital Over-the-Horizon Relay) -
   http://www.ietf.org/proceedings/65/slides/DTNRG-2.pdf

[6] CamMobSens - Cambridge University Pollution Monitoring Initiative -
   http://www.escience.cam.ac.uk/mobiledata/


                                                                                          36
References
[7] W. Moreira and P. Mendes, “Survey on opportunistic routing for delay tolerant
   networks,” Tech. Rep. SITI-TR-11-02, Research Unit in Informatics Systems and
   Technologies (SITI), University Lusofona, February, 2011.

[8] P. Hui, J. Crowcroft, E. Yoneki, BUBBLE Rap: Social-based Forwarding in Delay
   Tolerant Networks, Mobile Computing, IEEE Transactions on, 10 (11)(2011) 1576–
   1589.

[9] P. Costa, C. Mascolo, M. Musolesi, G. P. Picco, Socially-aware routing for publish-
   subscribe in delay-tolerant mobile ad hoc networks, Selected Areas in
   Communications, IEEE Journal on, 26 (5) (2008) 748–760.

[10] A. Mtibaa, M. May, M. Ammar, C. Diot, Peoplerank: Combining social and
  contact information for opportunistic forwarding, in: Proceedings of INFOCOM,
  San Diego, USA, March, 2010.

[11] T. Hossmann, T. Spyropoulos, F. Legendre, Know thy neighbor: Towards optimal
  mapping of contacts to social graphs for dtn routing, in: Proceedings of IEEE
  INFOCOM, San Diego, USA, March, 2010.


                                                                                     37
Using Social Information to
Improve Opportunistic Networking


                    Waldir Moreira
                waldir.junior@ulusofona.pt
                         Feb. 1st, 2012
                    SITI Brainstorm Meeting

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Using Social Information to Improve Opportunistic Networking

  • 1. Using Social Information to Improve Opportunistic Networking Waldir Moreira waldir.junior@ulusofona.pt Feb. 1st, 2012 SITI Brainstorm Meeting
  • 2. Agenda • Introduction • Application Environments • Routing over Opportunistic Networks • Our Work • Conclusions and Future Work 2
  • 4. Picture today • Users are eager for retrieving/providing information • Popularization of portable devices 4
  • 5. Straightforward Definition OppNets are highly dynamic, composed of mobile and static nodes (i.e., devices) and take advantages of opportunistic time- varying contacts among users carrying them to exchange information 5
  • 6. General OppNets Characteristics • Occasional contacts • Intermittent connectivity • Highly mobile and fixed nodes • Power-constrained devices • Possible nonexistence of e2e paths 6
  • 8. Different Environments • Disruptive environments: - Sparse scenarios where communication is established through sporadic contacts • Urban environments - Dense scenarios with communication suffering different interference levels 8
  • 9. Disruptive Environments Deep Space Communications • Purpose: provide communication means for manned/robotic exploration • Main challenges: very long delays, sparseness, shadow areas and spacecraft lifetime • Function: Information and commands are exchanged between landers/rovers and earth station through orbiters 9
  • 10. Disruptive Environments Deep Space Communications [1] News on Deep Space Networking [2] Mars Reconnaissance Orbiter 10
  • 11. Disruptive Environments Networks for Developing World • Purpose: provide asynchronous Internet access despite the scarce/expensive infrastructure • Main challenges: long delays and scarce/expensive infrastructure • Function: data is sent/retrieved either through USB stick carried by a motorbiker or via dial-up connection 11
  • 12. Disruptive Environments Networks for Developing World [3] S. Jain, K. Fall, R. Patra, Routing in a delay tolerant network, 2004 [4] News on Pigeon Carrier 12
  • 13. Disruptive Environments Zebranet • Purpose: Study zebra movements through collars carried by them • Main challenges: energy constraints • Function: collars opportunistically exchange GPS location later then obtained by scientists 13
  • 15. Disruptive Environments Tactical Military Networks • Purpose: establish quick communication means among military soldiers, vehicles, and aircrafts • Main challenges: high disruption and partition • Function: information is relayed among military units 15
  • 16. Disruptive Environments Tactical Military Networks [5] MITRE Corporation (C2 On-the-Move Network, Digital Over-the-Horizon Relay) 16
  • 17. Urban Environments Opportunistic Sensing • Purpose: gather information from sensing systems • Main challenges: short contact times • Function: sensor present in different devices gather information which is then collected mobile devices (i.e., custodian) to be transfered to the sensing system central 17
  • 18. Urban Environments Opportunistic Sensing [6] CamMobSens - Cambridge University Pollution Monitoring Initiative 18
  • 20. What is it about? Considers any contact among nodes and forwarding decisions are made using locally collected knowledge about node behavior to predict which nodes are likely to deliver a content or bring it closer to the destination 20
  • 21. 2000-2010 Analysis [7] W. Moreira and P. Mendes, “Survey on opportunistic routing for delay tolerant networks,” SITI, University Lusofona, February, 2011 21
  • 23. Major Routing Families [7] W. Moreira and P. Mendes, “Survey on opportunistic routing for delay tolerant networks,” SITI, University Lusofona, February, 2011 23
  • 24. Social Aspects: The New Trend • Since 2007 • Have shown great potential • Use social relationship • Much wiser decisions 24
  • 25. Replication-based Approaches Social Similarity • Community Detection: creation of communities considering people social relationships - Bubble Rap * Forwarding based on community and local/ global centrality [8] P. Hui, J. Crowcroft, E. Yoneki, BUBBLE Rap: Social-based Forwarding in Delay Tolerant Networks, 2011 25
  • 26. Replication-based Approaches Social Similarity • Shared Interests: nodes with the same interest as destination are good forwarders - SocialCast * predicted node’s co-location (probability of nodes being co-located with others) * change in degree of connectivity (mobility and changes in neighbor sets) [9] P. Costa, C. Mascolo, M. Musolesi, G. P. Picco, Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks, 2008 26
  • 27. Replication-based Approaches Social Similarity • Node Popularity: use of social information to generate ranks to nodes based on their position on a social graph - PeopleRank * Forwarding based on social ranking of nodes [10] A. Mtibaa, M. May, M. Ammar, C. Diot, Peoplerank: Combining social and contact information for opportunistic forwarding, 2010 27
  • 28. Our Work 28
  • 29. Motivation • Community detection, shared interests, node popularity • Communities are statically defined • Do not consider the age of contacts when computing the centrality • Strong assumptions • Full knowledge on social information is not enough • Some social metrics (e.g., betweenness centrality) can lead to node homogeneity [11] T. Hossmann, T. Spyropoulos, F. Legendre, Know thy neighbor: Towards optimal mapping of contacts to social graphs for dtn routing, 2010 29
  • 30. Our Proposal  dLife  People's daily life routine and their social ties to reach a clean representation of social interactions  Time-Evolving Contact Duration (TECD)  Weights social interactions based on statistical contact duration nodes have over time  TECD Importance (TECDi)  Estimates the importance of nodes 30
  • 32. Promising results Average Delivery Probability Created Scenario 1.0 0.8 0.6 0.4 0.2 Average Delivery Probability 0.0 Traces 1 day 2 day 4 day 1 week 3 week TTL 1.0 0.8 0.6 BubbleRap dLife Comm 0.4 dLife 0.2 0.0 1 day 2 day 4 day 1 week 3 week TTL 32
  • 33. Promising results Average Cost Created Scenario 1600 1400 1200 1000 # of replicas 800 600 400 200 Average Cost 0 Traces 1 day 2 day 4 day 1 week 3 week TTL 40 35 30 25 # of replicas 20 BubbleRap dLife Comm 15 dLife 10 5 0 1 day 2 day 4 day 1 week 3 week TTL 33
  • 34. Promising results Average Latency Created Scenario 45000 40000 35000 30000 Seconds 25000 20000 15000 10000 Average Latency 5000 Traces 0 1 day 2 day 4 day 1 week 3 week 45000 TTL 40000 35000 30000 Seconds 25000 BubbleRap 20000 dLife Comm dLife 15000 10000 5000 0 1 day 2 day 4 day 1 week 3 week TTL 34
  • 35. Conclusions and Future Work  Functions in separate had good overall performance  Their combination sure provided improvements  dLife is able to transcribe the dynamic behavior found on users' interactions into clean social representations  Plans  Improve it by introducing randomness and a stale- data removal scheme 35
  • 36. References [1] News on Deep Space Networking - http://www.engadget.com/2008/11/19/nasas-interplanetary-internet-tests-a- success-vint-cerf-triump/ [2] Mars Reconnaissance Orbiter - http://www.nasa.gov/mission_pages/MRO/news/mro-20060912.html [3] S. Jain, K. Fall, R. Patra, Routing in a delay tolerant network, in: Proceedings of the ACM SIGCOMM, Portland, USA, August, 2004. [4] News on Pigeon Carrier - http://www.dailymail.co.uk/news/article- 1212333/Pigeon-post-faster-South-Africas-Telkom.html [5] MITRE Corporation (US Marine Corps) (Presentation on C2 On-the-Move Network, Digital Over-the-Horizon Relay) - http://www.ietf.org/proceedings/65/slides/DTNRG-2.pdf [6] CamMobSens - Cambridge University Pollution Monitoring Initiative - http://www.escience.cam.ac.uk/mobiledata/ 36
  • 37. References [7] W. Moreira and P. Mendes, “Survey on opportunistic routing for delay tolerant networks,” Tech. Rep. SITI-TR-11-02, Research Unit in Informatics Systems and Technologies (SITI), University Lusofona, February, 2011. [8] P. Hui, J. Crowcroft, E. Yoneki, BUBBLE Rap: Social-based Forwarding in Delay Tolerant Networks, Mobile Computing, IEEE Transactions on, 10 (11)(2011) 1576– 1589. [9] P. Costa, C. Mascolo, M. Musolesi, G. P. Picco, Socially-aware routing for publish- subscribe in delay-tolerant mobile ad hoc networks, Selected Areas in Communications, IEEE Journal on, 26 (5) (2008) 748–760. [10] A. Mtibaa, M. May, M. Ammar, C. Diot, Peoplerank: Combining social and contact information for opportunistic forwarding, in: Proceedings of INFOCOM, San Diego, USA, March, 2010. [11] T. Hossmann, T. Spyropoulos, F. Legendre, Know thy neighbor: Towards optimal mapping of contacts to social graphs for dtn routing, in: Proceedings of IEEE INFOCOM, San Diego, USA, March, 2010. 37
  • 38. Using Social Information to Improve Opportunistic Networking Waldir Moreira waldir.junior@ulusofona.pt Feb. 1st, 2012 SITI Brainstorm Meeting