Automatic Weather Stations (AWSs) are embedded systems equipped with a number of sensors used to monitor harsh environments: glaciers and deserts. AWSs may also be equipped with some communication interfaces in order to enable remote access to data. These systems are generally far from power sources, and thus they are equipped with energy harvesting devices, wind turbines and solar panels, and storage devices, batteries. The design of an AWS represents a challenge, since designers have to maximize the sampled and transmitted data while considering the energy needs. We designed and implemented an energy-aware simulator of AWSs to support designers in the definition of the configuration of the system. The simulator relies on the Stochastic Activity Networks (SANs) formalism and has been developed using the Möbius tool. In this chapter we first show how we used SANs to model the components of an AWS, we then report results from validation experiments carried out by comparing the results of the simulator against a real-world AWS and finally show examples of its usage.
A practical approach to the sensitivity analysis for kinetic Monte Carlo simulation of heterogeneous catalysis
