Fast-performance simulation for Gossip-based Wireless Sensor Networks

Abstract

Gossip-based Wireless Sensor Networks (GWSNs) are complex systems of inherently random nature. Planning and designing GWSNs requires a fast and adequately accurate mechanism to estimate system performance. As a first contribution, we propose a performance analysis technique that simulates the gossip-based propagation of each single piece of data in isolation. This technique applies to GWSNs in which the dissemination of data from a specific sensor does not depend on dissemination of data generated by other sensors. We model the dissemination of a piece of data with a Stochastic-Variable Graph Model (SVGM). A SVGM is a weighted-graph abstraction in which the edges represent stochastic variables that model propagation delays between neighboring nodes. Latency and reliability performance properties are obtained efficiently through a stochastic shortest-path analysis on the SVGM using Monte Carlo (MC) simulation. The method is accurate and fast, applicable for both partial and complete system analysis. It outperforms traditional discrete-event simulation. As a second contribution, we propose a centrality-based stratification method that combines structural network analysis and MC partial simulation, to further increase efficiency of the system-level analysis while maintaining adequate accuracy. We analyzed the proposed performance evaluation techniques through an extensive set of experiments, using a real deployment and simulations at different levels of abstraction.