BOOK ON ROUTING IN
OPPORTUNISTIC NETWORKS
Chapter 2:
Social-aware Opportunistic Routing: the
New Trend
1Waldir
Moreira, 1Paulo Mendes
1SITILabs,
© SITILabs, University Lusófona, Portugal
University Lusófona
1
Goal of this Chapter
Introduce different opportunistic routing
approaches
Learn about existing opportunistic routing
taxonomies
Show how social information improves data
forwarding
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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-toend paths do not exist between communicating parties
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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
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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
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Opportunistic Routing Approaches
Different approaches
 Single-copy Routing
 Epidemic Routing
 Probabilistic-based Routing
• Frequency Encounters
• Aging Encounters
• Aging Messages
• Resource Allocation
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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)
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Existing Opportunistic Routing Taxonomies
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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
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Experimental Analysis
Goal
 Show how opportunistic routing can benefit from social
awareness
Done in two scenarios
 Heterogeneous (synthetic mobility models)
 Real human traces
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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
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Experimental Setup
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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)
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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
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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
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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
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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
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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
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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
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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.
© SITILabs, University Lusófona, Portugal
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BOOK ON ROUTING IN
OPPORTUNISTIC NETWORKS
Chapter 2:
Social-aware Opportunistic Routing: the
New Trend
1Waldir
Moreira, 1Paulo Mendes
1SITILabs,
© SITILabs, University Lusófona, Portugal
University Lusófona
21