Enabling Accurate Node Control in

radiographerfictionData Management

Oct 31, 2013 (3 years and 5 months ago)

63 views

© 2008 IBM Corporation

IBM T. J. Watson Research Center

Slide
1

Enabling Accurate Node Control in
Randomized Duty Cycling Networks

Kang
-
Won Lee*, Vasileios Pappas, Asser
Tantawi

IBM T. J. Watson Research Center

Research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defense and was accomplished under Agree
men
t Number

W911NF
-
06
-
3
-
0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as

representing the

official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry
of
Defense or the U.K.

Government.


2

ITA Consortium

Fundamental Research Program in Network and Information Science

International Technology Alliance in
Network and Information Sciences


Collaborative Alliance Managers/Consortium Managers


Jay Gowens (ARL)



Jack Lemon (MoD)


Dinesh Verma (IBM)



Dave Watson (IBM
-
UK)

Security Across a
System
-
of
-
Systems


Trevor Benjamin (Dstl)

Greg Cirincione (ARL)

John McDermid (York U)

Dakshi Agrawal (IBM)

Network Theory





Ananthram Swami (ARL)

Tom McCutcheon (Dstl)

Don Towsley (U Mass)

Kang
-
Won Lee (IBM)

Sensor Information
Processing


Tien Pham (ARL)

Gavin Pearson (Dstl)

Thomas La Porta (PSU)

Vic Thomas (Honeywell)

Distributed Coalition
Planning



Jitu Patel (Dstl)

Mike Strub (ARL)

Nigel Shadbolt (SHamp)

Graham Bent (IBM)


4

International Technology Alliance in
Network and Information Sciences


Collaborative Alliance Managers/Consortium Managers


Jay Gowens (ARL)



Jack Lemon (MoD)


Dinesh Verma (IBM)



Dave Watson (IBM
-
UK)

Security Across a
System
-
of
-
Systems


Trevor Benjamin (Dstl)

Greg Cirincione (ARL)

John McDermid (York U)

Dakshi Agrawal (IBM)

Network Theory





Ananthram Swami (ARL)

Tom McCutcheon (Dstl)

Don Towsley (U Mass)

Kang
-
Won Lee (IBM)

Sensor Information
Processing


Tien Pham (ARL)

Gavin Pearson (Dstl)

Thomas La Porta (PSU)

Vic Thomas (Honeywell)

Distributed Coalition
Planning



Jitu Patel (Dstl)

Mike Strub (ARL)

Nigel Shadbolt (SHamp)

Graham Bent (IBM)


Policy Based Security
Management


Calo, IBM

Energy Efficient
Security
Architectures and
Infrastructures


Paterson, Royal
Holloway

Trust and Risk
Management in
Dynamic Coalition
Environments


McDermid, York


Theoretical
Foundations for
Analysis/Design of
Wireless and Sensor
Networks


Towsley, U Mass

Interoperability of
Wireless Networks
and Systems


Lee, IBM

Hancock, RMR

Biologically
-
Inspired
Self
-
Organization in
Networks


Lio, Cambridge

Pappas, IBM


Quality of Information
of Sensor Data


Bisdikian, IBM

Task
-
Oriented
Deployment of
Sensor Data
Infrastructures


La Porta, Penn State

Complexity
Management of
Sensor Data
Infrastructures


Szymanski, RPI


Mission Adaptive
Collaborations


Poltrock, Boeing

Command Process
Transformation and
Analysis


Sieck, Klein Assoc

Shared Situational
Awareness and the
Semantic Battlespace
Infosphere


Shadbolt, Southhampton

Wagget, IBM


IBM T. J. Watson Research Center


© 2008 IBM Corporation


Slide
5


Invited Seminar at Tsinghua University, June 20, 2008

Wireless Sensor Networks

Embed

numerous distributed devices to
monitor and interact with physical world


Exploit
spatially and temporally dense,

in
situ,

sensing and actuation

Network

these devices so that they can
coordinate to perform higher
-
level
identification and tasks.


Requires
robust distributed systems

of
hundreds or thousands of devices.

[Estrin, Introduction to wireless sensor networks]

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
6

KOCSEA 2008, October 25, 2008

Duty Cycling in Wireless Sensor Networks


Power saving


Longevity of mission lifetime


Impacts the performance

Sensor coverage

Connectivity

Routing delay

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
7

KOCSEA 2008, October 25, 2008

Related Work


SPAN (Chen, 2001)

Local randomized decision to join a forwarding backbone based on the
estimate how much it will benefit the neighbors


GAF (Xu, 2001)

Sets up a virtual grid based on location information, and only one node in a
grid becomes active


STEM (Schurgers, 2002)

Nodes awaken sleeping neighbors when they need to forward data using
beacons on a dedicated signaling channel


NAPS (Godfrey, 2004)

Local randomized algorithm based on number of neighbors with an aim to
achieve global connectivity




IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
8

KOCSEA 2008, October 25, 2008

STAR:
s
patial
t
ransition
a
lgo
r
ithm

Z Z
Z



Z Z
Z



Z Z
Z



Z Z
Z



IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
9

KOCSEA 2008, October 25, 2008

STAR:
s
patial
t
ransition
a
lgo
r
ithm

Hmm… 3 out of 7

neighbors are awake.

Therefore I should sleep

for duration
T


Sleep duration
T

is selected based on (1) intrinsic parameter, (2)
extrinsic parameter and (3) state of its neighbors

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
10

KOCSEA 2008, October 25, 2008

STAR Duty Cycling Networks


Each node makes local decision

Sleep decision: where

Wake
-
up decision: where



We are interested in the steady state

What fraction of nodes will be active in a steady state?



Approach

Model the state of a duty cycling network as a spatial process



Intrinsic

parameter

External

factor

No. of awake/

sleeping

neighbors

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
11

KOCSEA 2008, October 25, 2008

Modeling a duty cycling network


spatial process


State of the network



for a network with set of nodes
V

and
E

where
|V| = n
and

|E| = e



Random field


steady state probability distribution


Markov r
andom

field


probability
only affected by neighbors

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
12

KOCSEA 2008, October 25, 2008

Steady state behavior


For a reversible Markov random field a simple general
solution exists [F. Kelly]


Let

α
(0) = 1,
α
(1) =
μ /
λ

λ
:
intrinsic rate of a node to transition to sleep state (0)

μ
:

intrinsic rate of a node to transition to wake
-
up state (1)



Equilibrium distribution



Three main parameters:
α

(intrinsic),
γ
,
δ

(external)

How do they affect the duty cycling performance?



IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
13

KOCSEA 2008, October 25, 2008

Impact of network size on the PDF

degree = 6,
α
=
γ

=
δ

= 1

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
14

KOCSEA 2008, October 25, 2008

Impact of the
α

parameter on the PDF

1000 nodes, degree = 6,
γ

=
δ

= 1

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
15

KOCSEA 2008, October 25, 2008

Impact of the
γ

and
δ

parameters

1000 nodes, degree = 6,
α
= 1

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
16

KOCSEA 2008, October 25, 2008

Impact of average node degree on the PDF

1000 nodes,
α
= 1,
γ

=
δ

= 1.05

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
17

KOCSEA 2008, October 25, 2008

Convergence speed

IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
18

KOCSEA 2008, October 25, 2008

Summary


ITA is a new venture for collaborative research in network
science



Presented an accurate node density control algorithm for a
randomized WSN

Recommendations

Use
α

to control the peak of the PDF

Choose small
γ

and
δ

for small variance

Start with large
λ
and
μ
for quick convergence


IBM T. J. Watson Research Center

© 2008 IBM Corporation

Slide
19

KOCSEA 2008, October 25, 2008

Thank
You

Merci

Grazie

Gracias

Obrigado

Danke

Japanese

English

French

Russian

German

Italian

Spanish

Brazilian Portuguese

Arabic

Traditional Chinese

Simplified Chinese

Hindi

Tamil

Thai

Korean

감사합니다