First Passage Percolation on Random Geometric Graphs and an Application to Shortest-Path Trees

2015 ◽  
Vol 47 (2) ◽  
pp. 328-354 ◽  
Author(s):  
C. Hirsch ◽  
D. Neuhäuser ◽  
C. Gloaguen ◽  
V. Schmidt

We consider Euclidean first passage percolation on a large family of connected random geometric graphs in the d-dimensional Euclidean space encompassing various well-known models from stochastic geometry. In particular, we establish a strong linear growth property for shortest-path lengths on random geometric graphs which are generated by point processes. We consider the event that the growth of shortest-path lengths between two (end) points of the path does not admit a linear upper bound. Our linear growth property implies that the probability of this event tends to zero sub-exponentially fast if the direct (Euclidean) distance between the endpoints tends to infinity. Besides, for a wide class of stationary and isotropic random geometric graphs, our linear growth property implies a shape theorem for the Euclidean first passage model defined by such random geometric graphs. Finally, this shape theorem can be used to investigate a problem which is considered in structural analysis of fixed-access telecommunication networks, where we determine the limiting distribution of the length of the longest branch in the shortest-path tree extracted from a typical segment system if the intensity of network stations converges to 0.

2015 ◽  
Vol 47 (02) ◽  
pp. 328-354
Author(s):  
C. Hirsch ◽  
D. Neuhäuser ◽  
C. Gloaguen ◽  
V. Schmidt

We consider Euclidean first passage percolation on a large family of connected random geometric graphs in the d-dimensional Euclidean space encompassing various well-known models from stochastic geometry. In particular, we establish a strong linear growth property for shortest-path lengths on random geometric graphs which are generated by point processes. We consider the event that the growth of shortest-path lengths between two (end) points of the path does not admit a linear upper bound. Our linear growth property implies that the probability of this event tends to zero sub-exponentially fast if the direct (Euclidean) distance between the endpoints tends to infinity. Besides, for a wide class of stationary and isotropic random geometric graphs, our linear growth property implies a shape theorem for the Euclidean first passage model defined by such random geometric graphs. Finally, this shape theorem can be used to investigate a problem which is considered in structural analysis of fixed-access telecommunication networks, where we determine the limiting distribution of the length of the longest branch in the shortest-path tree extracted from a typical segment system if the intensity of network stations converges to 0.


2012 ◽  
Vol 71 (1-2) ◽  
pp. 199-220 ◽  
Author(s):  
D. Neuhäuser ◽  
C. Hirsch ◽  
C. Gloaguen ◽  
V. Schmidt

2010 ◽  
Vol 42 (4) ◽  
pp. 936-952 ◽  
Author(s):  
Florian Voss ◽  
Catherine Gloaguen ◽  
Volker Schmidt

We consider spatial stochastic models, which can be applied to, e.g. telecommunication networks with two hierarchy levels. In particular, we consider Cox processes XL and XH concentrated on the edge set T(1) of a random tessellation T, where the points XL,n and XH,n of XL and XH can describe the locations of low-level and high-level network components, respectively, and T(1) the underlying infrastructure of the network, such as road systems, railways, etc. Furthermore, each point XL,n of XL is marked with the shortest path along the edges of T to the nearest (in the Euclidean sense) point of XH. We investigate the typical shortest path length C* of the resulting marked point process, which is an important characteristic in, e.g. performance analysis and planning of telecommunication networks. In particular, we show that the distribution of C* converges to simple parametric limit distributions if a scaling factor κ converges to 0 or ∞. This can be used to approximate the density of C* by analytical formulae for a wide range of κ.


2010 ◽  
Vol 42 (04) ◽  
pp. 936-952
Author(s):  
Florian Voss ◽  
Catherine Gloaguen ◽  
Volker Schmidt

We consider spatial stochastic models, which can be applied to, e.g. telecommunication networks with two hierarchy levels. In particular, we consider Cox processes X L and X H concentrated on the edge set T (1) of a random tessellation T, where the points X L,n and X H,n of X L and X H can describe the locations of low-level and high-level network components, respectively, and T (1) the underlying infrastructure of the network, such as road systems, railways, etc. Furthermore, each point X L,n of X L is marked with the shortest path along the edges of T to the nearest (in the Euclidean sense) point of X H . We investigate the typical shortest path length C * of the resulting marked point process, which is an important characteristic in, e.g. performance analysis and planning of telecommunication networks. In particular, we show that the distribution of C * converges to simple parametric limit distributions if a scaling factor κ converges to 0 or ∞. This can be used to approximate the density of C * by analytical formulae for a wide range of κ.


2008 ◽  
Vol 18 (6) ◽  
pp. 2300-2319 ◽  
Author(s):  
Jean-Baptiste Gouéré ◽  
Régine Marchand

2015 ◽  
Vol 107 ◽  
pp. 122-130 ◽  
Author(s):  
Christian Hirsch ◽  
David Neuhäuser ◽  
Catherine Gloaguen ◽  
Volker Schmidt

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