© SITILabs, University Lusófona, Portugal 1

Chapter 2: Social-aware Opportunistic

Routing: the New Trend

1Waldir Moreira, 1Paulo Mendes

1SITILabs, University Lusófona

BOOK ON ROUTING IN OPPORTUNISTIC NETWORKS

© SITILabs, University Lusófona, Portugal 2

Goal of this Chapter

Introduce different opportunistic routing approaches

Learn about existing opportunistic routing taxonomies

Show how social information improves data forwarding

© SITILabs, University Lusófona, Portugal 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

© SITILabs, University Lusófona, Portugal 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

© SITILabs, University Lusófona, Portugal 5

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

© SITILabs, University Lusófona, Portugal 6

Opportunistic Routing Approaches

Different approaches Single-copy Routing

Epidemic Routing

Probabilistic-based Routing• Frequency Encounters• Aging Encounters• Aging Messages• Resource Allocation

© SITILabs, University Lusófona, Portugal 7

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

© SITILabs, University Lusófona, Portugal 8

Existing Opportunistic Routing Taxonomies

© SITILabs, University Lusófona, Portugal 9

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

© SITILabs, University Lusófona, Portugal 10

Goal Show how opportunistic routing can benefit

from social awareness

Done in two scenarios Heterogeneous (synthetic mobility models) Real human traces

Experimental Analysis

© SITILabs, University Lusófona, Portugal 11

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

© SITILabs, University Lusófona, Portugal 12

Experimental Setup

© SITILabs, University Lusófona, Portugal 13

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

© SITILabs, University Lusófona, Portugal 14

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

© SITILabs, University Lusófona, Portugal 15

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

© SITILabs, University Lusófona, Portugal 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

© SITILabs, University Lusófona, Portugal 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

© SITILabs, University Lusófona, Portugal 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

© SITILabs, University Lusófona, Portugal 19

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

© SITILabs, University Lusófona, Portugal 20

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

© SITILabs, University Lusófona, Portugal 21

Chapter 2: Social-aware Opportunistic

Routing: the New Trend

1Waldir Moreira, 1Paulo Mendes

1SITILabs, University Lusófona

BOOK ON ROUTING IN OPPORTUNISTIC NETWORKS