The overall traffic of droplets in a network of microfluidic channels is strongly influenced by the liquid properties of the moving droplets. In particular, the effective hydrodynamic resistance of individual droplets plays a key role in their global behavior. Here we propose two simple and low-cost experimental methods for measuring this parameter by analyzing the dynamics of a regular sequence of droplets injected into an "asymmetric loop" network. The choice of a droplet taking either route through the loop is influenced by the presence of previous droplets that modulate the hydrodynamic resistance of the branches they are sitting in. We propose to extract the effective resistance of a droplet from easily observable time series, namely, from the choices the droplets make at junctions and from the interdroplet distances. This becomes possible when utilizing a recently proposed theoretical model based on a number of simplifying assumptions. Here we present several sets of measurements of the hydrodynamic resistance of droplets, expressed in terms of a "resistance length." The aim is twofold: (1) to reveal its dependence on a number of parameters, such as the viscosity, the volume of droplets, their velocity as well as the spacing between them. At the same time (2), by using a standard measurement technique, we compare the limitations of the proposed methods. As an important result of this comparison, we obtain the range of validity of the simplifying assumptions made in the theoretical model.

We propose a simple model to analyze the traffic of droplets in microfluidic "dual networks." Such functional networks which consist of two types of channels, namely, those accessible or forbidden to droplets, often display a complex behavior characteristic of dynamical systems. By focusing on three recently proposed configurations, we offer an explanation for their remarkable behavior. Additionally, the model allows us to predict the behavior in different parameter regimes. A verification will clarify fundamental issues, such as the network symmetry, the role of the driving conditions, and of the occurrence of reversible behavior. The model lends itself to a fast numerical implementation, thus can help designing devices, identifying parameter windows where the behavior is sufficiently robust for a device to be practically useful, and exploring new functionalities.