Dependability analysis of wireless sensor networks with active-sleep cycles and redundant nodes

ABSTRACT

Wireless Sensor Networks (WSN) are constituted of a large number of tiny sensor nodes randomly distributed over a geographical region. In order to reduce power consumption, battery-operated sensors undergo cycles of sleeping - active periods that reduce their ability to send/receive data. Starting from the Markov reward model theory, in this paper we present a dependability model to analyze the reliability of a sensor node. We also introduce a new dependability parameter, referred to as producibility, able to capture the capability of a sensor to accomplish its mission. Two different model solution techniques are proposed, one based on the evaluation of the accumulated reward distribution and the other based on an equivalent model based on non-Markovian Stochastic Petri nets. The obtained results are used to investigate the dependability of a whole WSN taking into account the presence of redundant nodes. Preliminary results are provided in order to highlight the advantages of the proposed technique.

References

1. K. Akkaya and M. Younis. A survey on routing protocols for wireless sensor networks. Elsevier Ad Hoc Networks, 3:325--349, 2005.
2. G. Anastasi, M. Conti, M. D. Francesco, and A. Passarella. How to prolong the lifetime of wireless sensor networks. Chapter 6 in Mobile Ad Hoc and Pervasive Communications, 2006.
3. D. Bruneo, M. Scarpa, A. Bobbio, D. Cerotti, and M. Gribaudo. Analytical modeling of swarm intelligence in wireless sensor networks through Markovian Agents. In VALUETOOLS09. ICST/ACM, October 2009.
4. G. Ciardo, R. German, and C. Lindemann. A characterization of the stochastic process underlying a stochastic petri net. IEEE Transactions on Software Engineering, 20(7):506--515, 1994.
5. L. Cloth, M. Jongerden, and B. Haverkort. Computing battery lifetime distributions. In 37th International Conference on Dependable Systems and Networks. (DSN'07), pages 780--789, 2007.
6. L. Donatiello and V. Grassi. On Evaluating the Cumulative Performance Distribution of Fault-Tolerant Systems. IEEE Transactions on Computer, 40(11):1301--1307, 1991.
7. C. Jaggle, J. Neidig, T. Grosch, and F. Dressler. Introduction to Model-based ReliabilityEvaluation of Wireless Sensor Networks. In 2nd IFAC Workshop on Dependable Control of Discrete Systems, pages 149--154, June 2009.
8. S. Mukhopadhyay, C. Schurgers, D. Panigrahi, and S. Dey. Model-Based Techniques for Data Reliability in Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 8(4):528--543, 2009.
9. T. S. Rappaport and T. Rappaport. Wireless Communications: Principles and Practice (2nd Edition). Prentice Hall PTR, December 2001.
10. R. Sahner, K. S. Trivedi, and A. Puliafito. Performance and reliability analysis of computer systems: an example based approach using the SHARPE software package. Kluwer Academic Publishers, Boston, 1995.
11. M. Scarpa, A. Puliafito, and S. Distefano. A parallel approach for the solution of non Markovian Petri nets. In 10th European PVM/MPI Users' Group Conference (EuroPVM/MPI03), pages 196--203, Venice, Italy, Sep. 2003. LNCS 2840.
12. K. S. Trivedi, A. Bobbio, G. Ciardo, R. German, A. Puliafito, and M. Telek. Non-markovian petri nets. SIGMETRICS Perform. Eval. Rev., 23(1):263--264, 1995.
13. F. Zhao and L. Guibas. Wireless sensor networks - An information processing approach. Morgan Kaufmann, 2004.

• General and reference

  • Cross-computing tools and techniques

• Networks

  • Network types