1. © SITILabs, University Lusófona, Portugal1 Chapter 2: Social-aware Opportunistic Routing: the New Trend 1 Waldir Moreira, 1 Paulo Mendes 1 SITILabs, University Lusófona BOOK ON ROUTING IN OPPORTUNISTIC NETWORKS

2. © SITILabs, University Lusófona, Portugal2 Goal of this Chapter  Introduce different opportunistic routing approaches  Learn about existing opportunistic routing taxonomies  Show how social information improves data forwarding

3. © SITILabs, University Lusófona, Portugal3 Introduction  Users want to be connected at all times  Produce and consume content (prosumers)  Devices capabilities contribute  Powerful (e.g., processing, storage)  Allow networks to be formed on-the-fly  Opportunistic routing provides the means  Allows the exchange of information even when end-to- end paths do not exist between communicating parties

4. © SITILabs, University Lusófona, Portugal4 Introduction  Issue: cope with link intermittency  Due to node mobility, power-saving schemes, physical obstacles, dark areas  Opportunistic routing relies on the  Store-carry-and-forward paradigm

5. © SITILabs, University Lusófona, Portugal5  There are different routing approaches  Ranging from network flooding to more elaborate replication schemes  A new trend emerges amongst solutions  Based on social similarity metrics (e.g., relationship, affiliation, importance, interests)  Focus of this chapter  Social-aware opportunistic routing  Great potential for improving opportunistic forwarding Introduction

6. © SITILabs, University Lusófona, Portugal6 Opportunistic Routing Approaches  Different approaches  Single-copy Routing  Epidemic Routing  Probabilistic-based Routing Frequency Encounters Aging Encounters Aging Messages Resource Allocation

7. © SITILabs, University Lusófona, Portugal7  Focus mostly on the efficiency  Achieve higher delivery rates  Spare network resources  The focus should also include  Analysis of the topological features (e.g., contact frequency and age, resource utilization, community formation, common interests, node popularity) Existing Opportunistic Routing Taxonomies

8. © SITILabs, University Lusófona, Portugal8 Existing Opportunistic Routing Taxonomies

9. © SITILabs, University Lusófona, Portugal9  Social similarity metrics gained attention  Human social behavior varies less than the one based on mobility  Based on social behavior abstracted from contacts between people, time spent with them, existing relationships New Opportunistic Routing Taxonomy

10. © SITILabs, University Lusófona, Portugal10  Goal  Show how opportunistic routing can benefit from social awareness  Done in two scenarios  Heterogeneous (synthetic mobility models)  Real human traces Experimental Analysis

11. © SITILabs, University Lusófona, Portugal11  Each experiment run ten times to provide results with a 95% confidence interval  Performance metrics  Average delivery probability Ratio between the total number of delivered and created messages  Average cost Number of replicas per delivered message  Average latency Time elapsed between message creation and delivery Experimental Methodology

12. © SITILabs, University Lusófona, Portugal12 Experimental Setup

13. © SITILabs, University Lusófona, Portugal13  Average Delivery Probability  dLife and dLifeComm consider users’ dynamic behavior Delivery rate over 74%  Bubble Rap is affected by limited buffer (2 MB) Results on Heterogeneous Scenario

14. © SITILabs, University Lusófona, Portugal14  Average Cost  Bubble Rap, dLife and dLifeComm have low cost as they use social similarity to replicate Cost of maximum 546, 319, and 319, respectively to perform a successful delivery Results on Heterogeneous Scenario

15. © SITILabs, University Lusófona, Portugal15  Average Latency  dLife and dLifeComm take longer to forward (strong social links or important nodes)  Bubble Rap chooses forwarders with weak ties Centrality does not capture dynamism Results on Heterogeneous Scenario

16. © SITILabs, University Lusófona, Portugal16 Results on Human Trace Scenario  Average Delivery Probability  Contact sporadicity affects Bubble Rap and dLife: Delivery 25.5% dLifeComm relies on node importance – Takes too long to reflect reality

17. © SITILabs, University Lusófona, Portugal17 Results on Human Trace Scenario  Average Cost  Bubble Rap, dLife and dLifeComm produced approx. 24.52, 24.56, and 28.79 replicas With few extra copies almost the same delivery performance as Spray & Wait

18. © SITILabs, University Lusófona, Portugal18 Results on Human Trace Scenario  Average Latency  Bubble Rap had similar behavior as in previous scenario  dLife and dLifeComm are affected by non-dynamism of user contact

19. © SITILabs, University Lusófona, Portugal19  Despite the challenges in the scenarios  Social-aware proposals that are able to capture dynamism of user behavior Good delivery performance with low associated cost and a subtle increase in latency Indeed have great potential in improving forwarding  More improvements  Consider point-to-multipoint communication  Increase even more performance of social-aware solutions Conclusions

20. © SITILabs, University Lusófona, Portugal20  Thanks are due to FCT for supporting the UCR (PTDC/EEA-TEL/103637/2008) project and Mr. Moreira’s PhD grant (SFRH/BD/62761/2009), and to the colleagues of the DTN-Amazon project for the fruitful discussions. Acknowledgements

21. © SITILabs, University Lusófona, Portugal21 Chapter 2: Social-aware Opportunistic Routing: the New Trend 1 Waldir Moreira, 1 Paulo Mendes 1 SITILabs, University Lusófona BOOK ON ROUTING IN OPPORTUNISTIC NETWORKS