# Query Processing in WSN: The

Mobile - Wireless

Nov 21, 2013 (4 years and 5 months ago)

77 views

Baljeet Malhotra

Supervisors

Ioanis Nikolaidis

Mario A. Nascimento

Communication Networks and Database Systems

Computing Science Department

Graduate Student’s Workshop on Network’s Research, Nov 17, 2009

Query Processing in WSN: The
Dilemma of Suppressions and Failures

In
-
Network Query Processing in WSN

Query

Result

In
-
network Processing

Decision Support system

Background

Queries

Variations of Top
-
k (Min, Max, Avg.)

Nearest Neighbor

Join queries

Techniques

Exploit the wireless broadcast nature of WSN

Aggregation, Pruning, Filtering ….

Schedules

Sensors should not be in ‘idle listening mode”

Contentious our slotted time

Failures

Unreliable communication

?

Top
-
k
Query

:
Problem Definition

Given a set of
N
sensors,
S =
{
s
i
:

i =1, 2,….., N
}

S
p,j

be the set of sensors that produced the
p
th

highest
value during the
j
th

round

v
(
S
p,j
) be the
p
th

highest value

The problem is to find:

Top
-
k values:
D
i

=
{
v
(
S
p,j
)

:

p =1, 2,….., k
}

Top
-
k sensors:
S
p,j

Top
-
k
Query Processing

A

Sink = Root

Shortest Path Tree (SPT)

Convergecast + Aggregation = TAG*

A

Sink = Root

*TAG, Madden et. al., OSDI’02, 2002

Convergecast + Aggregation

A

Sink = Root

Total # of messages used = n
-
1

Convergecast + Aggregation + Filtering

A

Sink = Root

τ (threshold) = min of top
-
k

Threshold (
τ

A

Sink = Root

Convergecast + Aggregation + Filtering

A

Sink = Root

send iff
v(
s
i
) ≥
τ
; or if top
-
k
node

A

Sink = Root

Total # of messages used = 8

A

Sink = Root

Dominating Set Tree (DST)

Total # of messages used = 4

Some Results

Varying
k

Tree topology makes a difference

EXTOK performs better on both synthetic and Intel
dataset

Synthetic data

Real data

Convergecast Scheduling

A

Sink = Root

TDMA slots => Precedence Constraints

Convergecast Scheduling

A

Sink = Root

TDMA slots => Conflict Free + Precedence Constraints

1

2

3

3

4

5

5

6

8

7

Some Results

Varying
Ψ

(node density)

SDA, Chen et. al, LNCS, vol. 3799, 2005

SAS, Wan, et. al., MOBIHOC, 2009

DAS, B. Yu et. al., INFOCOM, 2009

PAS, X. Yu et. al., SSDB, 2007

Synthetic data

Real data

Convergecast Scheduling + Filtering

A

Sink = Root

send iff
v(
s
i
) ≥
τ
; or if top
-
k
node

1

2

3

3

4

5

5

6

8

7

Convergecast Scheduling + Filtering + Failure

A

Sink = Root

send iff
v(
s
i
) ≥
τ
; or if top
-
k
node

1

2

3

3

4

5

5

6

8

7

Convergecast Scheduling + Failure Recovery

A

Sink = Root

1

2

3

3

4

5

5

6

8

7

Some Results

Application Perspective

Just Brodcast

Conclusions

Infrastructure

Clusters and Routing Trees

Dominating Sets

Techniques

Aggregation, Pruning, and Filtering

How to use them in the best possible way for a particular problem ?

Can we use one single infrastructure for every thing ?

Schedules

Every task must be done in a systematic fashion while minimizing the
response time

Failures

What happens when parts of our infrastructure breaks down ?

How and When to fix these problems ?

Acknowledgements

This research is partially supported by NSERC,
i
-
Core,
FGSR, VLDB, GSA, and Walter John Scholarship.

Contact
baljeet@cs.ualberta.ca

for more details.

THANK YOU !