Pervasive Social Computing : Algorithms andDeployments

Sonia Ben Mokhtar

Mobisys Seminar

28th October 2008

HeterogeneousPlatforms

Pervasive functionalities

10

Middleware for pervasive computing

Social Networks

Pervasive Social Computing (PSC)

ScenariosArrange a tennis gamearound the campus at

5pm, intermediate playerlevel, 1hour

I would like to have acoffee with otherbreastfeeding mums inmy neighborhood

I am looking for apostdoc/internship in anEnglish speakingcountry starting nextSeptember

I would like to sharea cab for going back

home

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

Related Work: Existing MiddlewareParadigms

• 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

Outline

• A Middleware for PSC (Overview)• Matching User Task Specifications

– Algorithms & Evaluation• Middleware Deployment Strategies

– Deployment Strategies & Evaluation• Conclusions & Future Work

A Middleware for PSC (Overview)

User Task Specification

UserId: totoActivity: TennisNbrPersons: 1Preferences: -A-->0.8 -B-->0.5 -C-->0.2Context Properties:5pm, UCL campus, 2hoursTTL = 2hours from now

Outline

• A Middleware for PSC (Overview)• Matching User Task Specifications

– Algorithms & Evaluation• Middleware Deployment Strategies

– Deployment Strategies & Evaluation• Conclusions & Future Work

Matching user tasks in PSC

T1 T1 T2 Tn…

Return Ti: Max(Utility(T,Ti))

Overall Satisfaction

T1 T2 Tn…

1. Generate all the possible pairs2. Compute Best CombinationC*={(Ti,Tj)}: Utility(C*)=Max3. Return Ti associated with T

T? T?

T? T? T1 T2 Tn…

1. Generate all the possible pairs2. Generate a Combination of the BestpairsC+= {(Ti,Tj)}: Utility((Ti,Tj)) >Utility((Ti+1,Tj+1))3. Return Ti associated with T

Local SatisfactionFIFO

Nearly Overall

Return T1

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

Utility wrt Matching Strategy

Distribution of the Satisfaction wrt MatchingStrategy

Overhead of the Matching Strategies

Delay to Answer wrt Matching Strategy

Matching Strategy: Discussion

• Resource Constraints and Social Network is notimportantFifo

• Resource Constraints and Social Network iscriticalLocal

• OtherwiseNearly or Combined

Outline

• A Middleware for PSC (Overview)• Matching User Task Specifications

– Algorithms & Evaluation• Middleware Deployment Strategies

– Deployment Strategies & Evaluation• Conclusions & Future Work

Middleware Deployment Strategies:Stationary Highly Connected Overlay

Middleware Deployment Strategies:Mobile Loosely Connected Overlay

Publication T1

Publication T2

Dissemination T1,T2Matching T1,T2

Notification T2 Notification T1

Middleware Deployment Strategies:Mobile Independent Brokers

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 ofbrokers,Traces)

– Communication Overhead (Number of messages, amount oftraffic)

Distribution of the Satisfaction wrtDeployment Strategy

Communication Overhead of the DeploymentStrategies

Distribution of the Satisfaction wrt Number ofBrokers and Mobility Traces

Middleware Deployment Strategies:Discussion

• If an infrastructure exists (e.g., campus)Stationary Overlay

• If no infrastructure and setting up one does notworth it (e.g., conference)Mobile OverlayA pre-analysis is worth doing to estimate the number of

brokers to deploy

Effect of the Connectivity of the SocialNetwork on the Utility

Outline

• A Middleware for PSC (Overview)• Matching User Task Specifications

– Algorithms & Evaluation• Middleware Deployment Strategies

– Deployment Strategies & Evaluation• Conclusions & Future Work

Conclusions & FW

• Pervasive Social Computing– Enable social interactivity among mobile users– Middleware for PSC support the scalable task

publication/dissemination/notification, social-context-aware taskmatching

• FW: The propagation of the social preferences by thebrokers– In the same activity, across different activities

• FW: Semantic specification and matching of user tasks– Emergent semantics vs Ontology-based approach