23,001 research outputs found

    Scaling of critical connectivity of mobile ad hoc communication networks

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    In this paper, critical global connectivity of mobile ad hoc communication networks (MAHCN) is investigated. We model the two-dimensional plane on which nodes move randomly with a triangular lattice. Demanding the best communication of the network, we account the global connectivity η\eta as a function of occupancy σ\sigma of sites in the lattice by mobile nodes. Critical phenomena of the connectivity for different transmission ranges rr are revealed by numerical simulations, and these results fit well to the analysis based on the assumption of homogeneous mixing . Scaling behavior of the connectivity is found as ηf(Rβσ)\eta \sim f(R^{\beta}\sigma), where R=(rr0)/r0R=(r-r_{0})/r_{0}, r0r_{0} is the length unit of the triangular lattice and β\beta is the scaling index in the universal function f(x)f(x). The model serves as a sort of site percolation on dynamic complex networks relative to geometric distance. Moreover, near each critical σc(r)\sigma_c(r) corresponding to certain transmission range rr, there exists a cut-off degree kck_c below which the clustering coefficient of such self-organized networks keeps a constant while the averaged nearest neighbor degree exhibits a unique linear variation with the degree k, which may be useful to the designation of real MAHCN.Comment: 6 pages, 6 figure

    Network formation by contact arrested propagation

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    We propose here a network growth model which we term Contact Arrested Propagation (CAP). One representation of the CAP model comprises a set of two-dimensional line segments on a lattice, propagating independently at constant speed in both directions until they collide. The generic form of the model extends to arbitrary networks, and, in particular, to three-dimensional lattices, where it may be realised as a set of expanding planes, halted upon intersection. The model is implemented as a simple and completely background independent substitution system. We restrict attention to one-, two- and three-dimensional background lattices and investigate how CAP networks are influenced by lattice connectivity, spatial dimension, system size and initial conditions. Certain scaling properties exhibit little sensitivity to the particular lattice connectivity but change significantly with lattice dimension, indicating universality. Suggested applications of the model include various fracturing and fragmentation processes, and we expect that CAP may find many other uses, due to its simplicity, generality and ease of implementation

    Spatial Fluid Limits for Stochastic Mobile Networks

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    We consider Markov models of large-scale networks where nodes are characterized by their local behavior and by a mobility model over a two-dimensional lattice. By assuming random walk, we prove convergence to a system of partial differential equations (PDEs) whose size depends neither on the lattice size nor on the population of nodes. This provides a macroscopic view of the model which approximates discrete stochastic movements with continuous deterministic diffusions. We illustrate the practical applicability of this result by modeling a network of mobile nodes with on/off behavior performing file transfers with connectivity to 802.11 access points. By means of an empirical validation against discrete-event simulation we show high quality of the PDE approximation even for low populations and coarse lattices. In addition, we confirm the computational advantage in using the PDE limit over a traditional ordinary differential equation limit where the lattice is modeled discretely, yielding speed-ups of up to two orders of magnitude

    Self-avoiding walks and connective constants in small-world networks

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    Long-distance characteristics of small-world networks have been studied by means of self-avoiding walks (SAW's). We consider networks generated by rewiring links in one- and two-dimensional regular lattices. The number of SAW's unu_n was obtained from numerical simulations as a function of the number of steps nn on the considered networks. The so-called connective constant, μ=limnun/un1\mu = \lim_{n \to \infty} u_n/u_{n-1}, which characterizes the long-distance behavior of the walks, increases continuously with disorder strength (or rewiring probability, pp). For small pp, one has a linear relation μ=μ0+ap\mu = \mu_0 + a p, μ0\mu_0 and aa being constants dependent on the underlying lattice. Close to p=1p = 1 one finds the behavior expected for random graphs. An analytical approach is given to account for the results derived from numerical simulations. Both methods yield results agreeing with each other for small pp, and differ for pp close to 1, because of the different connectivity distributions resulting in both cases.Comment: 7 pages, 5 figure

    Cross-over behaviour in a communication network

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    We address the problem of message transfer in a communication network. The network consists of nodes and links, with the nodes lying on a two dimensional lattice. Each node has connections with its nearest neighbours, whereas some special nodes, which are designated as hubs, have connections to all the sites within a certain area of influence. The degree distribution for this network is bimodal in nature and has finite variance. The distribution of travel times between two sites situated at a fixed distance on this lattice shows fat fractal behaviour as a function of hub-density. If extra assortative connections are now introduced between the hubs so that each hub is connected to two or three other hubs, the distribution crosses over to power-law behaviour. Cross-over behaviour is also seen if end-to-end short cuts are introduced between hubs whose areas of influence overlap, but this is much milder in nature. In yet another information transmission process, namely, the spread of infection on the network with assortative connections, we again observed cross-over behaviour of another type, viz. from one power-law to another for the threshold values of disease transmission probability. Our results are relevant for the understanding of the role of network topology in information spread processes.Comment: 12 figure
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