Enabling Accurate Node Control in Randomized Duty Cycling Networks

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. 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) Network Theory Ananthram Swami (ARL) Tom McCutcheon (Dstl) Don Towsley (U Mass) Kang-Won Lee (IBM) Security Across a System-of-Systems Trevor Benjamin (Dstl) Greg Cirincione (ARL) John McDermid (York U) Dakshi Agrawal (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) Network Theory Ananthram Swami (ARL) Tom McCutcheon (Dstl) Don Towsley (U Mass) Kang-Won Lee (IBM) Security Across a System-of-Systems Trevor Benjamin (Dstl) Greg Cirincione (ARL) John McDermid (York U) Dakshi Agrawal (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) Theoretical Foundations for Analysis/Design of Wireless and Sensor Networks Towsley, U Mass Policy Based Security Management Calo, IBM Mission Adaptive Collaborations Poltrock, Boeing Quality of Information of Sensor Data Bisdikian, IBM Command Process Transformation and Analysis Sieck, Klein Assoc Interoperability of Wireless Networks and Systems Lee, IBM Hancock, RMR Energy Efficient Security Architectures and Infrastructures Paterson, Royal Holloway Task-Oriented Deployment of Sensor Data Infrastructures La Porta, Penn State Trust and Risk Management in Dynamic Coalition Environments McDermid, York Biologically-Inspired Self-Organization in Networks Lio, Cambridge Pappas, IBM Complexity Management of Sensor Data Infrastructures Szymanski, RPI Shared Situational Awareness and the Semantic Battlespace Infosphere Shadbolt, Southhampton Wagget, IBM

5. 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. Duty Cycling in Wireless Sensor Networks • Power saving  Longevity of mission lifetime • Impacts the performance • Sensor coverage • Connectivity • Routing delay

7. 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. STAR: spatial transition algorithm Z Z Z … Z Z Z … Z Z Z … Z Z Z …

9. STAR: spatial transition algorithm Hmm… 3 out of 7 neighbors are awake. Therefore I should sleep for duration T… Sleep duration Tis selected based on (1) intrinsic parameter, (2) extrinsic parameter and (3) state of its neighbors

10. 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 No. of awake/ sleeping neighbors Intrinsic parameter External factor

11. Modeling a duty cycling network – spatial process • State of the network for a network with set of nodes Vand E where |V| = n and |E| = e • Random field steady state probability distribution • Markov random field probability only affected by neighbors

12. 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?

13. Impact of network size on the PDF degree = 6, α = γ = δ = 1

14. Impact of the αparameter on the PDF 1000 nodes, degree = 6, γ= δ= 1

15. Impact of the γand δparameters 1000 nodes, degree = 6, α = 1

16. Impact of average node degree on the PDF 1000 nodes, α = 1, γ= δ= 1.05

17. Convergence speed

18. 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 αto control the peak of the PDF • Choose small γand δfor small variance • Start with large λ and μ for quick convergence

19. Hindi Thai Traditional Chinese Gracias Russian Spanish Thank You Obrigado English Brazilian Portuguese Arabic Danke German Grazie Merci Simplified Chinese Italian French 감사합니다 Tamil Japanese Korean