© 2008 IBM Corporation IBM T. J. Watson Research Center Slide 1 Enabling Accurate Node Control in...

© 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


Research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defense and was accomplished under Agreement 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.

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ITA Consortium

Fundamental Research Program in Network and Information Science

International Technology Alliance in International Technology Alliance in Network and Information SciencesNetwork and Information Sciences

Collaborative Alliance Managers/Consortium Managers Jay Gowens (ARL) Jack Lemon (MoD) Dinesh Verma (IBM) Dave Watson (IBM-UK)



Security Across a Security Across a System-of-SystemsSystem-of-Systems

Trevor Benjamin (Dstl)Trevor Benjamin (Dstl)Greg Cirincione (ARL)Greg Cirincione (ARL)

John McDermid (York U)John McDermid (York U)Dakshi Agrawal (IBM)Dakshi Agrawal (IBM)




Network TheoryNetwork Theory

Ananthram Swami (ARL)Ananthram Swami (ARL)Tom McCutcheon (Dstl)Tom McCutcheon (Dstl)Don Towsley (U Mass)Don Towsley (U Mass)Kang-Won Lee (IBM)Kang-Won Lee (IBM)



Sensor Information Sensor Information ProcessingProcessing

Tien Pham (ARL)Tien Pham (ARL)Gavin Pearson (Dstl)Gavin Pearson (Dstl)

Thomas La Porta (PSU)Thomas La Porta (PSU)Vic Thomas (Honeywell)Vic Thomas (Honeywell)




Distributed Coalition Distributed Coalition PlanningPlanning

Jitu Patel (Dstl)Jitu Patel (Dstl)Mike Strub (ARL)Mike Strub (ARL)

Nigel Shadbolt (SHamp)Nigel Shadbolt (SHamp)Graham Bent (IBM)Graham Bent (IBM)




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Policy Based Security Management

Calo, IBMCalo, IBM

Policy Based Security Management

Calo, IBMCalo, IBM

Energy Efficient Security

Architectures and Infrastructures

Paterson, Royal Paterson, Royal HollowayHolloway

Energy Efficient Security

Architectures and Infrastructures

Paterson, Royal Paterson, Royal HollowayHolloway

Trust and Risk Management in

Dynamic Coalition Environments

McDermid, YorkMcDermid, York

Trust and Risk Management in

Dynamic Coalition Environments

McDermid, YorkMcDermid, York

Theoretical Foundations for

Analysis/Design of Wireless and Sensor

Networks

Towsley, U MassTowsley, U Mass

Theoretical Foundations for

Analysis/Design of Wireless and Sensor

Networks

Towsley, U MassTowsley, U Mass

Interoperability of Wireless Networks

and Systems

Lee, IBMLee, IBMHancock, RMRHancock, RMR

Interoperability of Wireless Networks

and Systems

Lee, IBMLee, IBMHancock, RMRHancock, RMR

Biologically-Inspired Self-Organization in

Networks

Lio, CambridgeLio, CambridgePappas, IBMPappas, IBM

Biologically-Inspired Self-Organization in

Networks

Lio, CambridgeLio, CambridgePappas, IBMPappas, IBM

Quality of Information of Sensor Data

Bisdikian, IBMBisdikian, IBM

Quality of Information of Sensor Data

Bisdikian, IBMBisdikian, IBM

Task-Oriented Deployment of Sensor Data

Infrastructures

La Porta, Penn StateLa Porta, Penn State

Task-Oriented Deployment of Sensor Data

Infrastructures

La Porta, Penn StateLa Porta, Penn State

Complexity Management of

Sensor Data Infrastructures

Szymanski, RPISzymanski, RPI

Complexity Management of

Sensor Data Infrastructures

Szymanski, RPISzymanski, RPI

Mission Adaptive Collaborations

Poltrock, BoeingPoltrock, Boeing

Mission Adaptive Collaborations

Poltrock, BoeingPoltrock, Boeing

Command Process Transformation and

Analysis

Sieck, Klein AssocSieck, Klein Assoc

Command Process Transformation and

Analysis

Sieck, Klein AssocSieck, Klein Assoc

Shared Situational Awareness and the

Semantic Battlespace Infosphere

Shadbolt, SouthhamptonShadbolt, SouthhamptonWagget, IBMWagget, IBM

Shared Situational Awareness and the

Semantic Battlespace Infosphere

Shadbolt, SouthhamptonShadbolt, SouthhamptonWagget, IBMWagget, 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]


© 2008 IBM CorporationSlide 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



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

…



STAR: spatial transition algorithm

Z Z Z …

Z Z Z …

Z Z Z …Z Z Z …



STAR: spatial transition algorithm

Hmm… 3 out of 7neighbors are awake.Therefore I should sleep for duration T…

Hmm… 3 out of 7neighbors 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



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

swtwetf )( )1('r

w wst

setf )()0('r

s

Intrinsic parameter

Externalfactor

No. of awake/sleepingneighbors



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

),...,,( 21 nssss

Random field

steady state probability distribution )1,0(:)( Ss

Markov random field

probability only affected by neighbors

)|()|( neighborjjGj ssPssP

},{ activesleepsi



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)1()0()1()( RRMBs

Three main parameters: α (intrinsic), γ, δ (external)

How do they affect the duty cycling performance? Three main parameters: α (intrinsic), γ, δ (external)

How do they affect the duty cycling performance?



Impact of network size on the PDF

degree = 6, α = γ = δ = 1degree = 6, α = γ = δ = 1



Impact of the α parameter on the PDF

1000 nodes, degree = 6, γ = δ = 11000 nodes, degree = 6, γ = δ = 1



Impact of the γ and δ parameters

1000 nodes, degree = 6, α = 11000 nodes, degree = 6, α = 1



Impact of average node degree on the PDF

1000 nodes, α = 1, γ = δ = 1.051000 nodes, α = 1, γ = δ = 1.05



Convergence speed



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



Thank You

MerciGrazie

Gracias

Obrigado

Danke

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