Hybrid Modeling Method Based on FSPN for Sensor Networks

Abstract

Sensor networks play a central role in the Internet of things, which attracts lots of attentions recently. Mathematical models are of much help to explore intricate scheduling on the sensor node or interactions between different sensor nodes. Although many existing approaches have shown that the sensor network behaves like a hybrid system, where discrete character and continuous character exist together, few of them have attempted to consider two characters together. In this paper, we propose a novel quantitative modeling framework based on Fluid Stochastic Petri nets (FSPNs), and provide comprehensive theoretical analysis to a typical sensor network example. Our modeling framework, which combines advantages of both Stochastic Petri Nets (SPNs) and Hybrid Functional Petri Nets (HFPNs), reflects the hybrid nature of sensor networks, and at the same time eases the problem of state space explosion. The modeling mechanism proposed in this paper constructs sensor network models that are comprised of both stochastic processes and fluid flow approximation technique. From the evaluation, it's shown that the new method performs well.