Evaluating wireless sensor node longevity through Markovian techniques

Wireless sensor networks are constituted of a large number of tiny sensor nodes randomly distributed over a geographical region. In order to reduce power consumption, nodes undergo active-sleep periods that, on the other hand, limit their ability to send/receive data. The aim of this paper is to analyze the longevity of a battery-powered sensor node. A battery discharge model able to capture both linear and non linear discharge processes is presented. Then, two different models are proposed to investigate the longevity, in terms of reliability, of sensor nodes with active-sleep cycles. The first model, well known in the literature, is based on the Markov reward theory and on the evaluation of the accumulated reward distribution. The second model, based on continuous phase type distributions and Kronecker algebra, represents the main contribution of the present work, since it allows to relax some assumptions of the Markov reward model, thus increasing its applicability to more concrete use cases. In the final part of the paper, the results obtained by applying the two techniques to a case study are compared in order to validate and highlight the benefits of our approach and demonstrate the utility of the proposed model in a quite complex and real scenario.

[1]  Antonio Puliafito,et al.  Dependability Evaluation with Dynamic Reliability Block Diagrams and Dynamic Fault Trees , 2009, IEEE Transactions on Dependable and Secure Computing.

[2]  M. Neuts,et al.  On the use of phase type distributions in reliability modelling of systems with two components , 1981 .

[3]  Roger A. Dougal,et al.  Dynamic lithium-ion battery model for system simulation , 2002 .

[4]  Sujit Dey,et al.  Model-Based Techniques for Data Reliability in Wireless Sensor Networks , 2009, IEEE Transactions on Mobile Computing.

[5]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[6]  Marco Aurélio Spohn,et al.  Simulation of Blind Flooding over Wireless Sensor Networks Based on a Realistic Battery Model , 2008, Seventh International Conference on Networking (icn 2008).

[7]  Richard Bellman,et al.  Introduction to Matrix Analysis , 1972 .

[8]  Jörg Neidig,et al.  Introduction to Model-based Reliability Evaluation of Wireless Sensor Networks , 2009 .

[9]  A. Bobbio,et al.  Kronecker representation of stochastic Petri nets with discrete PH distributions , 1998, Proceedings. IEEE International Computer Performance and Dependability Symposium. IPDS'98 (Cat. No.98TB100248).

[10]  Hamid Sharif,et al.  Lifetime Optimization for Wireless Sensor Networks Using the Nonlinear Battery Current Effect , 2009, 2009 IEEE International Conference on Communications.

[11]  Marcel F. Neuts,et al.  Matrix-geometric solutions in stochastic models - an algorithmic approach , 1982 .

[12]  Richard Bellman,et al.  Introduction to matrix analysis (2nd ed.) , 1997 .

[13]  Ioan Filip,et al.  Markov models for wireless sensor network reliability , 2009, 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing.

[14]  Theodore S. Rappaport,et al.  Wireless Communications: Principles and Practice (2nd Edition) by , 2012 .

[15]  Rafael Pérez-Ocón,et al.  Transient analysis of a repairable system, using phase-type distributions and geometric processes , 2004, IEEE Transactions on Reliability.

[16]  Roberto Passerone,et al.  A methodology for power consumption evaluation of wireless sensor networks , 2009, 2009 IEEE Conference on Emerging Technologies & Factory Automation.

[17]  Rafael Pérez-Ocón,et al.  A multiple warm standby system with operational and repair times following phase-type distributions , 2006, Eur. J. Oper. Res..

[18]  Boudewijn R. Haverkort,et al.  Computing Battery Lifetime Distributions , 2007, 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07).

[19]  L. Donatiello,et al.  On Evaluating the Cumulative Performance Distribution of Fault-Tolerant Computer Systems , 1991, IEEE Trans. Computers.

[20]  Sarma B. K. Vrudhula,et al.  An Analytical High-Level Battery Model for Use in Energy Management of Portable Electronic Systems , 2001, ICCAD.

[21]  Marco Gribaudo,et al.  Markovian agent modeling swarm intelligence algorithms in wireless sensor networks , 2012, Perform. Evaluation.

[22]  Juan Eloy Ruiz-Castro,et al.  Two models for a repairable two-system with phase-type sojourn time distributions , 2004, Reliab. Eng. Syst. Saf..

[23]  V. Battaglia,et al.  Electrochemical modeling of lithium polymer batteries , 2002 .

[24]  Francesco Longo,et al.  Applying Symbolic Techniques to the Representation of Non-Markovian Models with Continuous PH Distributions , 2009, EPEW.

[25]  L. Tu The Life and Works of , 2006 .

[26]  Arne Jensen,et al.  The life and works of A. K. Erlang , 1960 .

[27]  Mohamed F. Younis,et al.  A survey on routing protocols for wireless sensor networks , 2005, Ad Hoc Networks.

[28]  Kishor S. Trivedi,et al.  Performance And Reliability Analysis Of Computer Systems (an Example-based Approach Using The Sharpe Software , 1997, IEEE Transactions on Reliability.

[29]  Sarma Vrudhula,et al.  A model for battery lifetime analysis for organizing applications on a pocket computer , 2003, IEEE Trans. Very Large Scale Integr. Syst..

[30]  Leonidas J. Guibas,et al.  Wireless sensor networks - an information processing approach , 2004, The Morgan Kaufmann series in networking.

[31]  Aldo Cumani,et al.  ESP - A Package for the Evaluation of Stochastic Petri Nets with Phase-Type Distributed Transition Times , 1985, PNPM.

[32]  Antonio Puliafito,et al.  Energy control in dependable wireless sensor networks: a modelling perspective , 2011 .

[33]  G. Anastasi,et al.  How to Prolong the Lifetime of Wireless Sensor Networks , 2006 .

[34]  VrudhulaSarma,et al.  A model for battery lifetime analysis for organizing applications on a pocket computer , 2003 .

[35]  N. Limnios,et al.  Semi-Markov Processes and Reliability , 2012 .

[36]  Kishor S. Trivedi,et al.  Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package , 2012 .