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ABSTRACT

Technological advancements due to Moore's law have led to the proliferation of complex wireless sensor network (WSN) domains. One commonality across all WSN domains is the need to meet application requirements (i.e. lifetime, responsiveness, etc.) through domain specific sensor node design. Techniques such as sensor node parameter tuning enable WSN designers to specialize tunable parameters (i.e. processor voltage and frequency, sensing frequency, etc.) to meet these application requirements. However, given WSN domain diversity, varying environmental situations (stimuli), and sensor node complexity, sensor node parameter tuning is a very challenging task. In this paper, we propose an automated Markov Decision Process (MDP)-based methodology to prescribe optimal sensor node operation (selection of values for tunable parameters such as processor voltage, processor frequency, and sensing frequency) to meet application requirements and adapt to changing environmental stimuli. Numerical results confirm the optimality of our proposed methodology and reveal that our methodology more closely meets application requirements compared to other feasible policies.

REFERENCES

• 1: I. F. Akyildiz , W. Su , Y. Sankarasubramaniam , E. Cayirci, Wireless sensor networks: a survey, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.38 n.4, p.393-422, 15 March 2002 [doi>10.1016/S1389-1286(01)00302-4]
• 2: ATMEL, ATMEL ATmega128L 8-bit Microcontroller Datasheet, ATMEL Corporation, San Jose, California, March 2009, {Online} Available: http://www.atmel.com/dyn/resources/prod_documents/doc2467.pdf
• 3: I. Chades, M. Cros, F. Garcia, and R. Sabbadin, Markov Decision Process (MDP) Toolbox v2.0 for MATLAB, INRA Toulouse, INRA, France, February 2005, {Online}. Available: http://www.inra.fr/internet/Departements/MIA/T/MDPtoolbox/
• 4: P. K. Dutta , D. E. Culler, System software techniques for low-power operation in wireless sensor networks, Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design, p.925-932, November 06-10, 2005, San Jose, CA
• 5: Prabal Dutta , Mike Grimmer , Anish Arora , Steven Bibyk , David Culler, Design of a wireless sensor network platform for detecting rare, random, and ephemeral events, Proceedings of the 4th international symposium on Information processing in sensor networks, April 24-27, 2005, Los Angeles, California
• 6: K. Greene, Sensor Networks for Dummies, Technology Review (published by MIT), March 2006.
• 7: Honeywell, Honeywell 1- and 2- Axis Magenetic Sensors HMC1001/1002, and HMC1021/1022 Datasheet, Honeywell International Inc., Morristown, New Jersey, March 2009, {Online}. Available: http://www.ssec.honeywell.com/magnetic/datasheets/hmc1001-2_1021-2.pdf
• 8: M. Horton, Commercial Wireless Sensor Networks: Status, Issues and Challenges, Proc. of the IEEE Sensor and Ad Hoc Communications and Networks (SECON): Keynote Presentation, Santa Clara, California, October 2004.
• 9: I. Kadayif , M. Kandemir, Tuning In-Sensor Data Filtering to Reduce Energy Consumption in Wireless Sensor Networks, Proceedings of the conference on Design, automation and test in Europe, p.20852, February 16-20, 2004
• 10: Sachin Kogekar , Sandeep Neema , Brandon Eames , Xenofon Koutsoukos , Akos Ledeczi , Miklos Maroti, Constraint-guided dynamic reconfiguration in sensor networks, Proceedings of the 3rd international symposium on Information processing in sensor networks, April 26-27, 2004, Berkeley, California, USA [doi>10.1145/984622.984677]
• 11: Rex Min , Travis Furrer , Anantha Chandrakasan, Dynamic Voltage Scaling Techniques for Distributed Microsensor Networks, Proceedings of the IEEE Computer Society Annual Workshop on VLSI (WVLSI'00), p.43, April 27-28, 2000
• 12: A. Munir and A. Gordon--Ross, An MDP-based Application Oriented Optimal Policy for Wireless Sensor Networks, to be submitted to IEEE Transactions on Computers, 2009.
• 13: Padmanabhan Pillai , Kang G. Shin, Real-time dynamic voltage scaling for low-power embedded operating systems, Proceedings of the eighteenth ACM symposium on Operating systems principles, October 21-24, 2001, Banff, Alberta, Canada
• 14: Martin L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, Inc., New York, NY, 1994
• 15: S. Sridharan, and S. Lysecky, A First Step Towards Dynamic Profiling of Sensor-Based Systems, Proc. of IEEE Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), San Francisco, California, June 2008.
• 16: E. Stevens-Navarro, Y. Lin, and V. Wong, An MDP-based Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks, IEEE Transactions on Vehicular Technology, vol. 57, no. 2, pp. 1243--1254, March 2008.
• 17: Sameer Tilak , Nael B. Abu-Ghazaleh , Wendi Heinzelman, Infrastructure tradeoffs for sensor networks, Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, September 28-28, 2002, Atlanta, Georgia, USA [doi>10.1145/570738.570746]
• 18: Lin Yuan , Gang Qu, Design Space Exploration for Energy-Efficient Secure Sensor Network, Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures, and Processors, p.88, July 17-19, 2002

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Design studies

Additional Classification:
  C. Computer Systems Organization
  C.3 SPECIAL-PURPOSE AND APPLICATION-BASED SYSTEMS
      Subjects: Real-time and embedded systems
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Modeling techniques

General Terms:
Design, Performance

Keywords:
MDP, dynamic optimization, wireless sensor networks