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Social-aware Opportunistic Routing: Trends & Analysis

Explore different approaches to social-aware opportunistic routing and how they improve data forwarding in challenging scenarios. Learn about existing taxonomies and experimental analysis on heterogeneous and human trace scenarios.

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Social-aware Opportunistic Routing: Trends & Analysis

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

  2. Goal of this Chapter • Introduce different opportunistic routing approaches • Learn about existing opportunistic routing taxonomies • Show how social information improves data forwarding

  3. 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. 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. Introduction • 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

  6. Opportunistic Routing Approaches • Different approaches • Single-copy Routing • Epidemic Routing • Probabilistic-based Routing • Frequency Encounters • Aging Encounters • Aging Messages • Resource Allocation

  7. Existing Opportunistic Routing Taxonomies • 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)

  8. Existing Opportunistic Routing Taxonomies

  9. New Opportunistic Routing Taxonomy • 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

  10. Experimental Analysis • Goal • Show how opportunistic routing can benefit from social awareness • Done in two scenarios • Heterogeneous (synthetic mobility models) • Real human traces

  11. Experimental Methodology • 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

  12. Experimental Setup

  13. Results on Heterogeneous Scenario • Average Delivery Probability • dLife and dLifeComm consider users’ dynamic behavior • Delivery rate over 74% • Bubble Rap is affected by limited buffer (2 MB)

  14. Results on Heterogeneous Scenario • 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

  15. Results on Heterogeneous Scenario • 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

  16. 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. 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. 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. Conclusions • 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

  20. Acknowledgements • 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.

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

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