On Evaluating the Cumulative Performance Distribution of Fault-Tolerant Computer Systems
IEEE Transactions on Computers
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Performance and reliability analysis of computer systems: an example-based approach using the SHARPE software package
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
A Characterization of the Stochastic Process Underlying a Stochastic Petri Net
IEEE Transactions on Software Engineering
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
Computing Battery Lifetime Distributions
DSN '07 Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
Model-Based Techniques for Data Reliability in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Analytical modeling of swarm intelligence in wireless sensor networks through Markovian agents
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
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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.