Mobile Crowdsensingx

youthfulgleekingNetworking and Communications

Feb 17, 2014 (3 years and 1 month ago)

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CS 495

Application Development for Smart Devices

Mobile
Crowdsensing

Current State
and Future
Challenges


Mobile
Crowdsensing
.



Overview of
Crowdsensing

applications.



MCS:
Unique Characteristics

Introduction to Mobile
Crowdsensing


Mobile

C
rowdsensing

means

the

integration

of

sensors

that

can

be

used

for

gathering

materialistic

or

non
-
materialistic

information,

people

who

use

these

sensors

&

obviously

their

global

participation
.


Introduction to Mobile
Crowdsensing


Mobile

C
rowdsensing

means

the

integration

of

sensors

that

can

be

used

for

gathering

materialistic

or

non
-
materialistic

information,

people

who

use

these

sensors

&

obviously

their

global

participation
.


User at Front End

Introduction to Mobile
Crowdsensing


Mobile

C
rowdsensing

means

the

integration

of

sensors

that

can

be

used

for

gathering

materialistic

or

non
-
materialistic

information,

people

who

use

these

sensors

&

obviously

their

global

participation
.


User at Front End

Web Service at Back End

Community Phenomena &
Monitorization


Monitoring common
phenomenon…


Pollution
(air/noise) levels in
a neighborhood.



Real
-
time
traffic
patterns.



P
ot
holes on
roads.



Road
closures and transit
timings.



……


Participatory Sensing

Opportunistic Sensing

Users actively engage in the
data collection activity.

Users manually determine
how, when, what, where to
sample.

Higher burdens or costs.

Can
avoid phone context
issues.

Takes random sample which is
application defined.

Easy to gather large amount
data in small time.

Can’t avoid phone context
issues.

Lower burdens or costs if
contextual problems are
handled.

Filtering Data by Handling Privacy Issues & Localization.

Dataset is ready for research !!!

The Paradigms…

The Concept of “Internet of Things”…

“When

objects

can

both

sense

the

environment

and

communicate,

they

become

tools

for

understanding

complexity

and

responding

to

it

swiftly
.

What’s

revolutionary

in

all

this

is

that

these

physical

information

systems

are

now

beginning

to

be

deployed,

and

some

of

them

even

work

largely

without

human

intervention
.




---

(
McKinsey

&

Company,

2010
)

The Research Challenges of MCS…

Localized
Analytics

Resource
Limitations

Privacy

Aggregate
Analytics

Architecture

Localized
Analytics

Raw

sensing

data

is

collected

on

devices

and

local

analytics

process

it

to

produce

consumable

data

for

applications
.

After

privacy

preservation,

the

data

is

sent

to

the

backend

and

aggregate

analytics

will

further

process

it

for

different

applications
.

Resource
Limitations


How do
multiple applications on the same device
utilize



energy
, bandwidth, and computation resources


without
significantly affecting the data quality of


each
other
?


How
does scheduling of sensing
tasks



occur
across multiple devices with diverse sensing


capabilities
and availabilities (which can change


dynamically
)?

Privacy

Approaches :



A
nonymization

; which
removes any identifying
information



from
the sensor data before
sharing it
with a third party
.



Secure multiparty
computation, where cryptographic



techniques

are
used to transform the data to preserve


the
privacy of an
individual.

MCS : Unique Characteristics…

This is a double sided sword…….

The intelligence and mobility of
humans can be leveraged to help
applications collect higher quality or
semantically complex data that may
otherwise require sophisticated
hardware and software
.

On

the

other

hand
,

humans

naturally

have

privacy

concerns

and

personal

preferences

that

are

not

necessarily

in

the

best

interests

of

MCS

applications

but

applications

have

to

live

within

these

constraints
.

References

1 .
Mobile
Crowdsensing
: Current State
and Future Challenges.



by
Raghu K. Ganti, Fan Ye, and Hui Lei



IBM
T. J. Watson Research Center, Hawthorne, NY

2.
Mobile Crowd
Sensing:An

Approach
to Smarter Cities.



by
Róbert

Szabó


Dept
. of Telecommunications and Media Informatics


Budapest
University of Technology and Economics