A framework for probabilistic numerical evaluation of sensor networks
Pierre Leone, Andrea Roli, Paul Albuquerque, Christian Mazza · ACM Journal of Experimental Algorithmics · 2007
In this paper we show how to use stochastic estimation methods to investigate topological properties of sensor networks as well as the behavior of dynamical processes on these networks. The framework is particularly important to study problems for which no theoretical results are known, or cannot be directly applied in practice, for instance, when only asymptotic results are available. We also interpret Russo's formula in the context of sensor networks and thus obtain practical information on their reliability. As a case study, we analyze a localization protocol for wireless sensor networks and validate our approach by numerical experiments. Finally, we mention three applications of our approach: estimating the number of pivotal sensors in a real network, minimizing the number of such sensors for robustness purposes during the network design and estimating the distance between successive localized positions for mobile sensor networks.