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© 2008 IBM Corporation IBM T. J. Watson Research Center Slide 1 Enabling Accurate Node Control in Randomized Duty Cycling Networks Kang-Won Lee*, Vasileios.

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Presentation on theme: "© 2008 IBM Corporation IBM T. J. Watson Research Center Slide 1 Enabling Accurate Node Control in Randomized Duty Cycling Networks Kang-Won Lee*, Vasileios."— Presentation transcript:

1 © 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 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.

2 2 ITA Consortium Fundamental Research Program in Network and Information Science

3 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) 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) 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) 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) 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) Distributed Coalition Planning Jitu Patel (Dstl) Mike Strub (ARL) Nigel Shadbolt (SHamp) Graham Bent (IBM)

4 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) 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) 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) 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) 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) Distributed Coalition Planning Jitu Patel (Dstl) Mike Strub (ARL) Nigel Shadbolt (SHamp) Graham Bent (IBM) Policy Based Security Management Calo, IBM Policy Based Security Management Calo, IBM Energy Efficient Security Architectures and Infrastructures Paterson, Royal Holloway Energy Efficient Security Architectures and Infrastructures Paterson, Royal Holloway Trust and Risk Management in Dynamic Coalition Environments McDermid, York Trust and Risk Management in Dynamic Coalition Environments McDermid, York Theoretical Foundations for Analysis/Design of Wireless and Sensor Networks Towsley, U Mass Theoretical Foundations for Analysis/Design of Wireless and Sensor Networks Towsley, U Mass Interoperability of Wireless Networks and Systems Lee, IBM Hancock, RMR Interoperability of Wireless Networks and Systems Lee, IBM Hancock, RMR Biologically-Inspired Self-Organization in Networks Lio, Cambridge Pappas, IBM Biologically-Inspired Self-Organization in Networks Lio, Cambridge Pappas, IBM Quality of Information of Sensor Data Bisdikian, IBM Quality of Information of Sensor Data Bisdikian, IBM Task-Oriented Deployment of Sensor Data Infrastructures La Porta, Penn State Task-Oriented Deployment of Sensor Data Infrastructures La Porta, Penn State Complexity Management of Sensor Data Infrastructures Szymanski, RPI Complexity Management of Sensor Data Infrastructures Szymanski, RPI Mission Adaptive Collaborations Poltrock, Boeing Mission Adaptive Collaborations Poltrock, Boeing Command Process Transformation and Analysis Sieck, Klein Assoc Command Process Transformation and Analysis Sieck, Klein Assoc Shared Situational Awareness and the Semantic Battlespace Infosphere Shadbolt, Southhampton Wagget, IBM Shared Situational Awareness and the Semantic Battlespace Infosphere Shadbolt, Southhampton Wagget, IBM

5 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 5Invited 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]

6 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 6KOCSEA 2008, October 25, 2008 Duty Cycling in Wireless Sensor Networks  Power saving  Longevity of mission lifetime  Impacts the performance Sensor coverage Connectivity Routing delay

7 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 7KOCSEA 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  …

8 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 8KOCSEA 2008, October 25, 2008 STAR: spatial transition algorithm Z Z Z …

9 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 9KOCSEA 2008, October 25, 2008 STAR: spatial transition algorithm Hmm… 3 out of 7 neighbors are awake. Therefore I should sleep for duration T … 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

10 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 10KOCSEA 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

11 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 11KOCSEA 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 random field probability only affected by neighbors

12 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 12KOCSEA 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? Three main parameters: α (intrinsic), γ, δ (external) How do they affect the duty cycling performance?

13 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 13KOCSEA 2008, October 25, 2008 Impact of network size on the PDF degree = 6, α = γ = δ = 1

14 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 14KOCSEA 2008, October 25, 2008 Impact of the α parameter on the PDF 1000 nodes, degree = 6, γ = δ = 1

15 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 15KOCSEA 2008, October 25, 2008 Impact of the γ and δ parameters 1000 nodes, degree = 6, α = 1

16 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 16KOCSEA 2008, October 25, 2008 Impact of average node degree on the PDF 1000 nodes, α = 1, γ = δ = 1.05

17 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 17KOCSEA 2008, October 25, 2008 Convergence speed

18 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 18KOCSEA 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

19 IBM T. J. Watson Research Center © 2008 IBM Corporation Slide 19KOCSEA 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

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