Fast simulation methods to predict wireless sensor network performance
Proceedings of the 6th ACM symposium on Performance evaluation of wireless …, 2009•dl.acm.org
With the increasing capabilities of Wireless Sensor Networks (WSN), complexity and
expectation of the WSN applications increase as well. In order to make design-space
exploration possible, it is necessary to have fast models that provide adequate insight in
system behavior. In this paper, we propose a highly abstracted, hierarchical, system-level
modeling method for WSN. Based on the model properties, fast simulation techniques can
be applied. First, an abstract discrete event simulation based on a Probabilistic Graph Model …
expectation of the WSN applications increase as well. In order to make design-space
exploration possible, it is necessary to have fast models that provide adequate insight in
system behavior. In this paper, we propose a highly abstracted, hierarchical, system-level
modeling method for WSN. Based on the model properties, fast simulation techniques can
be applied. First, an abstract discrete event simulation based on a Probabilistic Graph Model …
With the increasing capabilities of Wireless Sensor Networks (WSN), complexity and expectation of the WSN applications increase as well. In order to make design-space exploration possible, it is necessary to have fast models that provide adequate insight in system behavior. In this paper, we propose a highly abstracted, hierarchical, system-level modeling method for WSN. Based on the model properties, fast simulation techniques can be applied. First, an abstract discrete event simulation based on a Probabilistic Graph Model (PGM) is introduced. Then, a fast Monte Carlo simulation approach is proposed for speeding up the simulation process. This approach combines Stochastic-Variable Graph Models (SVGM), providing a high level of abstraction, with shortest path calculations. As a case study, a temperature mapping application in a gossip-based WSN is used, showing a good accuracy of the model predictions.
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