  Individual-based hybrid modelling of spatially distributed systems is usually
expensive. Here, we consider a hybrid system in which mobile agents spread over
the space and interact with each other when in close proximity. An
individual-based model for this system needs to capture the spatial attributes
of every agent and monitor the interaction between each pair of them. As a
result, the cost of simulating this model grows exponentially as the number of
agents increases. For this reason, a patch-based model with more abstraction
but better scalability is advantageous. In a patch-based model, instead of
representing each agent separately, we model the agents in a patch as an
aggregation. This property significantly enhances the scalability of the model.
In this paper, we convert an individual-based model for a spatially distributed
network system for wild-life monitoring, ZebraNet, to a patch-based stochastic
HYPE model with accurate performance evaluation. We show the ease and
expressiveness of stochastic HYPE for patch-based modelling of hybrid systems.
Moreover, a mean-field analytical model is proposed as the fluid flow
approximation of the stochastic HYPE model, which can be used to investigate
the average behaviour of the modelled system over an infinite number of
simulation runs of the stochastic HYPE model.
