View
56
Download
0
Category
Tags:
Preview:
Citation preview
http://copelabs.ulusofona.pt
Human-centered Computing Lab
Dynamics of Social-aware Pervasive Networks
Waldir Moreira and Paulo Mendeswaldir.junior@ulusofona.pt
March 27th, 20154th IEEE PerCom Workshop on the Impact of Human Mobility in Pervasive Systems and Applications (PerMoby 2015)St. Louis, USA
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Agenda
Introduction
Human Behavior-aware Aggregation
HBA Analysis
Impact of HBA on Opportunistic Forwading
Conclusions and Future Work
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Introduction
Opp. forwarding influenced by user mobility and social engagement
Aggregation algorithms identify relevant nodes and links
If oblivious about dynamism
– Nodes with same degree
– Links represent a mere encounter
When dynamism is considered
– Nodes with different degrees
– Social relevant links
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Introduction
Complex Network Analysis used to study graph properties
– Opp. forwarding performs well over small-world/scale-free graphs
Previous work study the dynamics of underlying graphs, but
– Social level is a mere product of age and frequency of contacts
– Social aggregation based on graph density (finding optimal density)
– Looks at the global network behavior (lower granularity)
However, user behavior is rather intricate
– Users interact differently throughout their daily routines
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Introduction
Social aggregation upon such a dynamic behavior
– Social level has more to it (age/frequency of contacts vs. time spent socially engaging)
– Patterns in user behavior exist and are influential
– Varying users’ social interactions and their respective mobility are of paramount importance
Thus, we focus on the time-evolving property of user social behavior
– Human behavior-aware aggregation
– Mobility patterns in different moments in time
– To understand the impact on the operation of opp. forwarding
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Human Behavior-aware Aggregation
Takes into account
– Daily sample: time interval in which users have similar behavior
– Social intensity: duration of contacts between nodes
– Time-evolving social ties: variation of users’ behavior
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
HBA Analysis
Done with the Gephi v0.8.21 and Cytoscape v2.8.32 tools
Over two CRAWDAD human traces
– Cambridge, which comprises a group of 36 students carrying iMote devices during a two-month period in Cambridge, UK
– MIT, which comprises 97 Nokia 6600 smart phones distributed among the students and staff of this institution
Goal
– Identify the type of complex network formed with HBA in different time periods of a user daily routine
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
HBA Analysis
Time-evolving property is imperative (Cambridge traces)
– If overlooked, homogeneity trend in terms of node degree
– If considered, highlights only the socially well-connected nodes
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
HBA Analysis
Complex network features (Small-world features)
– SW properties vary with HBA but are still there
– Nodes are less clustered and avg. path length is higher
– Lower, and yet relevant, number of links
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Parameters Values
Simulator Opportunistic Network Environment (ONE)
Routing Proposals HBA-based fwd, dLife, dLifeComm, Bubble Rap, Rank and Epidemic
Scenarios CRAWDAD Cambridge trace CRAWDAD MIT trace
Simulation Time 1036800 sec 17020800 sec
# of Nodes 36 97
Generated messages 6000 78000
Node Interface Bluetooth
Node Buffer 2 MB
Message TTL Length of experiments
Message Size 1 – 100 kB
Impact of HBA on Opportunistic Forwading
HBA-based Forwarding (HF): weight of social link dLife: social weight, node importance dLifeComm: social weight, node importance, net. community Bubble Rap: network community, node centrality Rank: node importance Epidemic: pure contact-based forwarding
Understand the impact on opp. forwarding when operating overHBA-based graphs
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Impact of HBA on Opportunistic Forwading
Average latency reduction of 5.1% for all forwarding approaches
Average cost
– Reduction of 44% for approaches based on social weights or communities (HF, dLife, Bubble Rap)
– Social-oblivious and node degree-based approaches are highly penalized (68% increase)
• Epidemic, ignores dynamics and spreading is eased by nodes being well conected (giant component, SF)
• dLifeComm and Rank, significant number of nodes with degree higher than the average
Average delivery probability
– Subtle decrease: up to 3.6% and 4.3% for the Cambridge and MIT
– Data mule effect for approaches relying on social weights (HF, dLife, and dLifeComm): carriers have difficulty in finding nodes with a higher social weight → direct delivery
Waldir Moreira, waldir.junior@ulusofona.pt http://copelabs.ulusofona.pt
Conclusions and Future Work
More coarseness in the analysis of the dynamics of user social behavior
– Social aggregation-based graphs must take this into account
– Social-oblivious and node degree-based approaches are punished with significant cost
HBA does have a positive effect on the performance of opp. forwarding
– Reflects the dynamics of pervasive networks, presenting small-world properties with edges reflecting high social intensity
– Occasional data muling for social weight-based approaches
Future work
– HBA with other forwarding schemes and other sets of human traces
– Compare the intensity-based aggregation with density-based aggregation: social weights vs. age/or frequency of contact
Recommended