© 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 ChapterIntroduce different opportunistic routing
approachesLearn 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