Transmission and navigation on disordered lattice networks, directed spanning forests and Brownian web
This work addresses the need for more realistic models of spatially dependent point fields in communication networks, such as wireless sensor networks, by moving beyond the standard independent and uniformly distributed node assumption.
The authors tackled the problem of modeling stochastic networks beyond the standard Poisson point process by investigating directed spanning forests on randomly perturbed lattices, showing that in low disorder the forest almost surely forms a single tree in 2D and 3D, and in 2D, the paths converge under diffusive scaling to the Brownian web.
Stochastic networks based on random point sets as nodes have attracted considerable interest in many applications, particularly in communication networks, including wireless sensor networks, peer-to-peer networks and so on. The study of such networks generally requires the nodes to be independently and uniformly distributed as a Poisson point process. In this work, we venture beyond this standard paradigm and investigate the stochastic geometry of networks obtained from \textit{directed spanning forests} (DSF) based on randomly perturbed lattices, which have desirable statistical properties as a models of spatially dependent point fields. In the regime of low disorder, we show in 2D and 3D that the DSF almost surely consists of a single tree. In 2D, we further establish that the DSF, as a collection of paths, converges under diffusive scaling to the Brownian web.