STOCHASTIC OPTIMIZATION FOR ENVIRONMENTALLY POWERED WSNS USING MDP MODELS WITH MULTI-EPOCH ACTIONS

Alexandru E. Şuşu

2010

Abstract

The controller of an environmentally powered wireless sensor node (WSN) seeks to maximize the quality of the data measurements and to communicate frequently with the network, while balancing the uncertain energy intake with the consumption. To devise such system manager we use the Markov Decision Process (MDP) optimization framework. However, our problem has physical characteristics that are not captured in the standard MDP model: namely, the radio interface takes a non-negligible amount of time to synchronize with the network before starting to transmit the acquired data, which translates into MDP actions spanning over multiple epochs. Optimizing without considering this multi-epoch actions requirement results in suboptimal MDP policies, which, under certain conditions described in the paper, waste on average 50% of the radio activity. Therefore, we incorporate this new constraint in the MDP formulation, and obtain an optimal policy that performs on average 83% better than a standard MDP policy. This solution outperforms also some heuristic policies we use for comparison by 14% and 154%.

References

  1. Anastasi, G., Conti, M., Di Francesco, M., and Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Netw., 7(3):537-568.
  2. Barrenetxea, G., Dubois-Ferriere, H., Meier, R., and Selker, J. (2006). A weather station for SensorScope. In Demo Session, In Information Processing in Sensor Networks (IPSN 2006).
  3. Dubois-Ferrière, H., Meier, R., Fabre, L., and Metrailler, P. (2006). TinyNode: A Comprehensive Platform for Wireless Sensor Network Applications. In Information Processing in Sensor Networks (IPSN 2006).
  4. Gyselinckx, B., Hoof, C. V., Ryckaert, J., Yazicioglu, R. F., Fiorini, P., and Leonov, V. (18-21 Sept. 2005). Human++: Autonomous wireless sensors for body area networks. In Custom Integrated Circuits Conference, 2005. Proceedings of the IEEE 2005 , vol., no.pp. 13- 19.
  5. Hu, Q. and Yue, W. (2007). Markov Decision Processes with Their Applications. Advances in Mechanics and Mathematics, v. 14. Springer, Dordrecht.
  6. Jiang, X., Polastre, J., and Culler, D. E. (2005). Perpetual environmentally powered sensor networks. In IPSN, pages 463-468.
  7. Kansal, A., Potter, D., and Srivastava, M. B. (2004). Performance aware tasking for environmentally powered sensor networks. SIGMETRICS Perform. Eval. Rev., 32(1):223-234.
  8. Kwiatkowska, M., Norman, G., and Parker, D. (2004). Prism 2.0: A tool for probabilistic model checking. QEST, 00:322-323.
  9. Moser, C., Thiele, L., Brunelli, D., and Benini, L. (2008). Approximate control design for solar driven sensor nodes. In HSCC 7808: Proceedings of the 11th international workshop on Hybrid Systems, pages 634-637, Berlin, Heidelberg. Springer-Verlag.
  10. Nfaoui, H., Essiarab, H., and Sayigh, A. (2003). A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco. Renewable Energy 29 1407-1418.
  11. Niyato, D., Hossain, E., and Fallahi, A. (2007). Sleep and wakeup strategies in solar-powered wireless sensor/mesh networks: Performance analysis and optimization. IEEE Transactions on Mobile Computing, 6(2):221-236.
  12. Paradiso, J. A. and Starner, T. (2005). Energy scavenging for mobile and wireless electronics. Pervasive Computing, IEEE, 4(1):18-27.
  13. Poggi, P., Notton, G., Muselli, M., and Louche, A. (2000). Stochastic study of hourly total solar radiation in Corsica using a Markov model. International Journal of Climatology, Volume 20, Issue 14, Pages 1843 - 1860.
  14. Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. WileyInterscience.
  15. Roundy, S., Leland, E. S., Baker, J., Carleton, E., Reilly, E., Lai, E., Otis, B., Rabaey, J. M., Wright, P. K., and Sundararajan, V. (2005). Improving power output for vibration-based energy scavengers. Pervasive Computing, IEEE, 4(1):28-36.
  16. S¸ us¸u, A. E., Acquaviva, A., Atienza, D., and Micheli, G. D. (2008). Stochastic modeling and analysis for environmentally powered wireless sensor nodes. Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops, 2008. WiOPT 2008. 6th International Symposium on, pages 125-134.
  17. Twidell, J. W. and Weir, A. D. (1986). Renewable energy resources.
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Paper Citation


in Harvard Style

E. Şuşu A. (2010). STOCHASTIC OPTIMIZATION FOR ENVIRONMENTALLY POWERED WSNS USING MDP MODELS WITH MULTI-EPOCH ACTIONS . In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8425-00-3, pages 238-243. DOI: 10.5220/0002940802380243


in Bibtex Style

@conference{icinco10,
author={Alexandru E. Şuşu},
title={STOCHASTIC OPTIMIZATION FOR ENVIRONMENTALLY POWERED WSNS USING MDP MODELS WITH MULTI-EPOCH ACTIONS},
booktitle={Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2010},
pages={238-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002940802380243},
isbn={978-989-8425-00-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - STOCHASTIC OPTIMIZATION FOR ENVIRONMENTALLY POWERED WSNS USING MDP MODELS WITH MULTI-EPOCH ACTIONS
SN - 978-989-8425-00-3
AU - E. Şuşu A.
PY - 2010
SP - 238
EP - 243
DO - 10.5220/0002940802380243