Hasibur Rahman

Enabling Scalable Publish/Subscribefor Logical-Clustering in

Crowdsourcing via MediaSense

Science and Information Conference 2014August 27-29, 2014 | London UK

Department of Computer and Systems Sciences (DSV)Stockholm University

Overview• Introduction

• Background

• Motivation

• Research Problem

• Our Solution

• Advantages and Analysis

• Conclusions and Future Work

Introduction• Crowdsourcing

• Increase of Information

• Vast IoT sources

• Logical-Clustering

• Efficient Management of Context Information (CI)

• Filters out similar context from distributed sources

• MediaSense

• A scalable distributed IoT platform for CI sharing

Background• Spontaneous human participation i.e. crowdsourcing is pivotal for future pervasive

computing (Franco- 2011)

• The surge of social networks, mobile devices, Internet or Web-enabled services have enabled unprecedented level of human participation in crowdsourcing which has been branded as “human-in-the-loop-sensing” or citizen sensor networks (Boulos et al. -2011), Sheth -2009)

• Ericsson predicts that 50-500 billion mobile devices will be in use by 2020 (Ericsson - 2013)

• Ericsson envisions 5G as enabler for Networked Society (Ericsson - 2013)

• This necessitates proper management of CI so that resources can be shared from remote places

• Logical-clustering has been proposed as opposed to physical clustering to efficiently manage CI (Rahman et al. - 2013)

Crowdsourcing•People

•Pervasive devices

• Internet or Web-enabled services

•Surrounding things

•Context Information

Motivation•Sharing heterogenous CI obtained from distributed

sources

•Publish/Subscribe (PubSub) has emerged as an efficient means of sharing ubiquitous CI

•By leveraging the PubSub in the crowdsourcing model can unravel the challenge of sharing CI in real-time

What is the problem?

Can the distributed MediaSense platform (Kanter et al., 2009) be used as scalable PubSub model in real-time?If it does then how does this approach differ from other approaches?"

We have proposed MediaSense as a potential solution to the above research questions

How can the context-IDs in logical-clustering be shared efficiently in real-time?How can we synchronize logical-sink?

Our SolutionAn entity registers as UCI in MediaSense

Each logical-sink registers itself as a UCI and associated context-IDs as its data

Logical-sinks are synchronized by registering physical sinks as UCIs

Our Approach

Our advantagesReal-time,Distributed- no central point of failure issue

Fast, Efficient, Scalable

Memory efficient

Our advantages

74%

74%

74% improvement compared to existing

MediaSense

# of published context-IDs

Current MediaSense

Modified MediaSense % improvement

1000 7.34 ms 4.17 ms 76

10000 8.93 ms 5.37 ms 66

100000 10.74 ms 6.23 ms 72

200000 11.65 ms 6.69 ms 74

AnalysisFor both Published and Subscribed items

3537 messages/sec if it is run only for one second and over 9000 if just published for logical-sink

PubSub messages/sec lowers by one-third while magnitude is increased by ten-fold

Analysis86%

Subscription matching only

For a hundred-fold increase 86% increase in matching duration

AnalysisSubscription matching only for a single context-ID

The one-millionth context-ID took 8.76 ms to match with the published context-IDs

Analysis

99%

99%

99% improvement compared to PARDES for 2 million context-

IDs

PARDES increasing; MediaSense increase is minimal

Analysis (Scalability)# of

context-IDs

Le Subscribe (Counting)

MediaSense % improvement

500 K 85 ms 14.76 ms 476

1 million 350 ms 16.22 ms 2058

# of context-

IDs

Le Subscribe (Counting)

MediaSense % improvement

15 K 621 3151 407

1 million 7 91 1200

2058%

2058% improvement compared to Le

Subscribe in terms of subscription matching

1200%

1200% improvement in PubSub messages/sec

Analysis

451%

163%

163%&

451%

MediaSense betters Le Subscribe and ToPSS respectively by 163%

and minimum by 451%

ConclusionsMediaSense is feasible as PubSub model in crowdsourcing especially in logical-clustering

It is fast- requires only 9.59 ms to match two-millionth published context-ID

Scales well compared to other PubSub model

Efficient and no centralization

Occupies only 185.97 MB memory to store 5 million context-IDs

Future WorkUCI discovery

Performance evaluation on devices with limited computational capabilities

Adaptability and awareness

Security might be a concern; we will look into this

+ 46 (0) 70 7463968

Contact Hasibur Rahman

hasibur.rahman021

hasibur@dsv.su.se

twitter.com/hasiburrahman29

facebook.com/SuZon.Hasibur.Rahman

ACKNOWLEDGMENT

The work is partially supported by funding from the European Union FP7 MobiS project. The outcome of this research will be used later in the project.

Q & A!

Thank You!

Credits

mobiS logo

www.mobis-euproject.eu/

IMAGE CREDITS

Q & A image

http://www.openlounge.org/lunargame/one-question-infinite-answers/