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Opportunistic Routing Based on Daily Routines


    Waldir Moreira, Paulo Mendes, and Susana Sargento
                         waldir.junior@ulusofona.pt
                                 June 25th, 2012
 6th IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC 2012)
                              San Francisco, USA
Agenda
• Introduction
• Motivation
• Our Proposal: dLife
• Evaluation
• Results
• Conclusions and Future Work



                                2
Introduction
• Powerful devices
• Spontaneous networks
• Opportunistic contacts
 - Intermittent connectivity




                               3
Motivation
• Many routing solutions
 - epidemic, encounter history, social
 aspects ...
• Instability of the created proximity graphs
• Dynamism of users’ behavior
• Daily life routines



                                            4
Our Proposal: dLife
• To capture the dynamics of the network
 represented by time-evolving social ties
 between pair of nodes
• Two utility functions
 - Time-Evolving Contact Duration (TECD)
 - TECD Importance (TECDi)



                                            5
Our Proposal: dLife




                      6
Our Proposal: dLife
(1)           A
                      w(B,x)
                                   B




      If Mx   Buffer(B) and w(B,x) > w(A,x)
(2)                    Mx
              A                    B


                   Otherwise
                      I(B)
(3)           A                    B



                  If I(B) > I(A)
                        Mx
(4)           A                    B



                                              7
Our Proposal: dLifeComm
(1)                A
                           w(B,x)
                                        B




      If Mx   Buffer(B) and B.sameComm(x) and w(B,x) > w(A,x)
(2)                         Mx
                   A                    B


                        Otherwise
                           I(B)
(3)                A                    B



                       If I(B) > I(A)
                             Mx
(4)                A                    B



                                                                8
Evaluation
Parameters                                                       Values
Simulator                                      Opportunistic Network Environment (ONE)
Routing Proposals                                  Bubble Rap, dLife and dLifeComm
Scenarios                              Heterogeneous Mobility                Trace Cambridge (CRAWDAD)
Simulation Time                             1036800 sec                             1000000 sec
# of Nodes                              150 (people/vehicles)                        36 (people)
Mobility Models               Working Day, Bus, Shortest Path Map Based               Human
Node Interface                   Wi-Fi (Rate: 11 Mbps / Range: 100 m)                Bluetooth
Node Buffer                                                       2 MB
Message TTL                                           1, 2, 4 days, 1 and 3 weeks
Message Size                                                    1 – 100 kB
Generated Messages                                                6000
K-Clique, k                                         5 (Bubble Rap and dLifeComm)
K-Clique, familiarThreshold                      700 sec (Bubble Rap and dLifeComm)
Daily Samples                                          24 (dLife and dLifeComm)



                                                                                                   9
Results




Heterogenous scenario                  Cambridge traces
-   dLife up to 39.5%                  -   dLife up to 31.5%
-   dLifeComm up to 31.2%              -   dLifeComm up to 31.3%
-   Bubble Rap (Global centrality)     -   Network dynamics (daily routines)
-   Few nodes (~17%) high centrality   -   Local centrality


                                                                       10
Results




Heterogenous scenario                 Cambridge traces
-   dLife up to 78% less              -   dLife up to 55% less
-   dLifeComm up to 68% less          -   dLifeComm up to 50.5% less
-   High social strength/importance   -   Variable patterns of contacts
-   Bubble rap further replicates     -   Forwarders not often available


                                                                       11
Results




Heterogenous scenario                   Cambridge traces
-   dLife up to 48.3% less              -   dLife up to 83.7% less
-   dLifeComm up to 46.1% less          -   dLifeComm up to 84.7% less
-   Smarter forwarding decisions        -   Smaller, well connected groups
-   Bubble Rap (weak ties to destin.)   -   Bubble Rap (Centrality not real)


                                                                          12
Conclusions and
Future Work
• Dynamism of users’ social daily behavior
 => wiser forwarding decisions
• Centrality presented higher impact
 => does not capture reality

• Next steps
                             Internet-Draft
    Information-Centric
                            DTNRG Meeting
     version of dLife
                          Vancouver, July 2012


                                                 13
Acknowledgements

To FCT for financial support via PhD grant
 (SFRH/BD/62761/2009) and UCR project
 (PTDC/EEA-TEL/103637/2008)




                                         14
Opportunistic Routing Based on Daily Routines


    Waldir Moreira, Paulo Mendes, and Susana Sargento
                         waldir.junior@ulusofona.pt
                                 June 25th, 2012
 6th IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC 2012)
                              San Francisco, USA

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Opportunistic Routing Based on Daily Routines

  • 1. Opportunistic Routing Based on Daily Routines Waldir Moreira, Paulo Mendes, and Susana Sargento waldir.junior@ulusofona.pt June 25th, 2012 6th IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC 2012) San Francisco, USA
  • 2. Agenda • Introduction • Motivation • Our Proposal: dLife • Evaluation • Results • Conclusions and Future Work 2
  • 3. Introduction • Powerful devices • Spontaneous networks • Opportunistic contacts - Intermittent connectivity 3
  • 4. Motivation • Many routing solutions - epidemic, encounter history, social aspects ... • Instability of the created proximity graphs • Dynamism of users’ behavior • Daily life routines 4
  • 5. Our Proposal: dLife • To capture the dynamics of the network represented by time-evolving social ties between pair of nodes • Two utility functions - Time-Evolving Contact Duration (TECD) - TECD Importance (TECDi) 5
  • 7. Our Proposal: dLife (1) A w(B,x) B If Mx Buffer(B) and w(B,x) > w(A,x) (2) Mx A B Otherwise I(B) (3) A B If I(B) > I(A) Mx (4) A B 7
  • 8. Our Proposal: dLifeComm (1) A w(B,x) B If Mx Buffer(B) and B.sameComm(x) and w(B,x) > w(A,x) (2) Mx A B Otherwise I(B) (3) A B If I(B) > I(A) Mx (4) A B 8
  • 9. Evaluation Parameters Values Simulator Opportunistic Network Environment (ONE) Routing Proposals Bubble Rap, dLife and dLifeComm Scenarios Heterogeneous Mobility Trace Cambridge (CRAWDAD) Simulation Time 1036800 sec 1000000 sec # of Nodes 150 (people/vehicles) 36 (people) Mobility Models Working Day, Bus, Shortest Path Map Based Human Node Interface Wi-Fi (Rate: 11 Mbps / Range: 100 m) Bluetooth Node Buffer 2 MB Message TTL 1, 2, 4 days, 1 and 3 weeks Message Size 1 – 100 kB Generated Messages 6000 K-Clique, k 5 (Bubble Rap and dLifeComm) K-Clique, familiarThreshold 700 sec (Bubble Rap and dLifeComm) Daily Samples 24 (dLife and dLifeComm) 9
  • 10. Results Heterogenous scenario Cambridge traces - dLife up to 39.5% - dLife up to 31.5% - dLifeComm up to 31.2% - dLifeComm up to 31.3% - Bubble Rap (Global centrality) - Network dynamics (daily routines) - Few nodes (~17%) high centrality - Local centrality 10
  • 11. Results Heterogenous scenario Cambridge traces - dLife up to 78% less - dLife up to 55% less - dLifeComm up to 68% less - dLifeComm up to 50.5% less - High social strength/importance - Variable patterns of contacts - Bubble rap further replicates - Forwarders not often available 11
  • 12. Results Heterogenous scenario Cambridge traces - dLife up to 48.3% less - dLife up to 83.7% less - dLifeComm up to 46.1% less - dLifeComm up to 84.7% less - Smarter forwarding decisions - Smaller, well connected groups - Bubble Rap (weak ties to destin.) - Bubble Rap (Centrality not real) 12
  • 13. Conclusions and Future Work • Dynamism of users’ social daily behavior => wiser forwarding decisions • Centrality presented higher impact => does not capture reality • Next steps Internet-Draft Information-Centric DTNRG Meeting version of dLife Vancouver, July 2012 13
  • 14. Acknowledgements To FCT for financial support via PhD grant (SFRH/BD/62761/2009) and UCR project (PTDC/EEA-TEL/103637/2008) 14
  • 15. Opportunistic Routing Based on Daily Routines Waldir Moreira, Paulo Mendes, and Susana Sargento waldir.junior@ulusofona.pt June 25th, 2012 6th IEEE WoWMoM Workshop on Autonomic and Opportunistic Communications (AOC 2012) San Francisco, USA