1. Pervasive Social Computing : Algorithms and
Deployments
Sonia Ben Mokhtar
Mobisys Seminar
28th October 2008

2. Pervasive Social Computing (PSC)
Social Networks
Pervasive
functionalities
Middleware
for pervasive
computing
Heterogeneous
Platforms
10

3. I would like to have a
coffee with other
Scenarios breastfeeding mums in
my neighborhood
Arrange a tennis game
around the campus at
5pm, intermediate player
level, 1hour
I am looking for a
postdoc/internship in an
English speaking
country starting next
September
I would like to share
a cab for going back
home

4. Issues and Key Challenges
• Issues
– Environment Heterogeneity & Dynamics
– Social-based user centrism
– Distributed, Multi-activity social network
– Context-awareness
– Semantic-awareness
– Privacy
• Challenges
– Middleware hosted in some (all) devices responsible for :
• Specification of user tasks (social-, semantic-aware)
• Disseminating user tasks (scalable, privacy- and context-aware)
• Matching user tasks (social-, semantic- and context-aware)
• Notifying the users of matching results

5. Related Work: Existing Middleware
Paradigms
• Tuple Space (distributed shared memory)
– Stateful, non-scalable
• Event-based (pub-sub)
– Proactive
– Persistency of subscriptions vs volatility of user tasks
• RPC-based (SOC)
– Persistency of services vs volatility of user tasks
– Service requester and provider roles are merged

6. Outline
• A Middleware for PSC (Overview)
• Matching User Task Specifications
– Algorithms & Evaluation
• Middleware Deployment Strategies
– Deployment Strategies & Evaluation
• Conclusions & Future Work

7. A Middleware for PSC (Overview)
User Task Specification
UserId: toto
Activity: Tennis
NbrPersons: 1
Preferences:
-A-->0.8
-B-->0.5
-C-->0.2
Context Properties:
5pm, UCL campus, 2hours
TTL = 2hours from now

8. Outline
• A Middleware for PSC (Overview)
• Matching User Task Specifications
– Algorithms & Evaluation
• Middleware Deployment Strategies
– Deployment Strategies & Evaluation
• Conclusions & Future Work

9. Matching user tasks in PSC
FIFO Local Satisfaction
T? T1 T? T1 T2 … Tn
Return T1 Return Ti: Max(Utility(T,Ti))
Overall Satisfaction Nearly Overall
T? T1 T2 … Tn T? T1 T2 … Tn
1. Generate all the possible pairs 1. Generate all the possible pairs
2. Compute Best Combination 2. Generate a Combination of the Best
C*={(Ti,Tj)}: Utility(C*)=Max pairs
3. Return Ti associated with T C+= {(Ti,Tj)}: Utility((Ti,Tj)) >
Utility((Ti+1,Tj+1))
3. Return Ti associated with T

10. Evaluation of the Matching Strategies
• Mobility Traces: MIT
• Social Network: Advogato
• Scenario:
– A node is elected to act as a broker (most popular)
– Each time a node encounters the broker: Task Publication
– When a node meets the broker again it is notified of the answer if any
(matching, expiry)
• Measurements:
– Accuracy (generated utility, distribution of the utility)
– Computational Overhead, delay to answer

11. Utility wrt Matching Strategy

12. Distribution of the Satisfaction wrt Matching
Strategy

13. Overhead of the Matching Strategies

14. Delay to Answer wrt Matching Strategy

15. Matching Strategy: Discussion
• Resource Constraints and Social Network is not
important
 Fifo
• Resource Constraints and Social Network is
critical
 Local
• Otherwise
 Nearly or Combined

16. Outline
• A Middleware for PSC (Overview)
• Matching User Task Specifications
– Algorithms & Evaluation
• Middleware Deployment Strategies
– Deployment Strategies & Evaluation
• Conclusions & Future Work

17. Middleware Deployment Strategies:
Stationary Highly Connected Overlay

18. Middleware Deployment Strategies:
Mobile Loosely Connected Overlay
Publication T2
Dissemination T1,T2 Notification T2 Notification T1
Matching T1,T2
Publication T1

19. Middleware Deployment Strategies:
Mobile Independent Brokers

20. Evaluation of the Deployment Strategies
• Mobility Traces: MIT, Cambridge
• Social Network: Advogato, MovieLens, LastFM
• Scenario:
– N brokers are elected in the Network (popularity)
– Each time a node encounters a brokers: Task Publication
– When a node meets the broker again it is notified of the answer if
any (matching, expiry)
• Measurements:
– Accuracy (distribution of utility wrt: strategy, number of
brokers,Traces)
– Communication Overhead (Number of messages, amount of
traffic)

21. Distribution of the Satisfaction wrt
Deployment Strategy

22. Communication Overhead of the Deployment
Strategies

23. Distribution of the Satisfaction wrt Number of
Brokers and Mobility Traces

24. Middleware Deployment Strategies:
Discussion
• If an infrastructure exists (e.g., campus)
 Stationary Overlay
• If no infrastructure and setting up one does not
worth it (e.g., conference)
 Mobile Overlay
 A pre-analysis is worth doing to estimate the number of
brokers to deploy

25. Effect of the Connectivity of the Social
Network on the Utility

26. Outline
• A Middleware for PSC (Overview)
• Matching User Task Specifications
– Algorithms & Evaluation
• Middleware Deployment Strategies
– Deployment Strategies & Evaluation
• Conclusions & Future Work

27. Conclusions & FW
• Pervasive Social Computing
– Enable social interactivity among mobile users
– Middleware for PSC support the scalable task
publication/dissemination/notification, social-context-aware task
matching
• FW: The propagation of the social preferences by the
brokers
– In the same activity, across different activities
• FW: Semantic specification and matching of user tasks
– Emergent semantics vs Ontology-based approach