Local empathy provides global minimization of congestion in communication networks

Efficient routing for spatial networks

In many complex networks, the main task is to transfer load from sources to destinations. Therefore, the network throughput is a very important indicator to measure the network performance. In order to improve the network throughput, researchers have proposed many effective routing strategies. Spatial networks, as a class of complex networks, exist widely in the real-world. However, the existing routing strategies in complex networks cannot achieve good results when applied in spatial networks. Therefore, in this paper, we propose a new degree-location ( D L) routing strategy to improve the throughput of spatial networks. In this routing strategy, the load transmitted from sources to destinations will bypass the nodes with high degrees and the nodes located close to the center of region. Simulations on homogeneous and heterogeneous spatial networks show that the D L routing strategy proposed in this paper can effectively improve the throughput of the network. The result of this paper can help the routing design of spatial networks and may find applications in many real-world spatial networks to improve the transmission performance.

Simulations suggest that navigation software may not be as efficient as expected for city traffic

We suggest a theoretical framework to study the dynamics of an open city, with cars entering at a certain rate and leaving as they reach their destinations. In particular, we assess through simulations some unexpected consequences of the massive use of GPS (global positioning system) navigation systems in the overall dynamics. One of our main interest is to identify what type of measurements would be the most relevant for an experimental study of this system, specifically, the ones useful for city traffic administrators. To do so, we solve the microdynamics using a cellular automaton model considering three different navigation strategies based on the minimization of the individual paths (unweighted strategy) or travel times (weighted strategies). Although the system is inherently stochastic, we found in our simulations an equivalent saddle-node bifurcation for all strategies where the input rate acts as a bifurcation parameter. There is also evidence of additional bifurcations for travel time minimization based strategies. Although we found that weighted strategies are more efficient in terms of car motion, there is a destabilization phenomenon that makes, in an unexpected way, a variation of the unweighted strategy more optimal at certain densities from the fuel efficiency of the overall city traffic point of view. These results bring new insight into the intrinsic dynamics of cities and the perturbations that individual traffic routing can produce on the city as a whole.

Does following optimized routes for single cars improve car routing?

We study the impact of deserting a pre-established path, determined by a navigation software, on the overall city traffic. To do so, we consider a cellular automaton model for vehicular traffic, where the cars travel between two randomly assigned points in the city following three different navigation strategies based on the minimization of the individual paths or travel times. We found, in general, that, above a critical car density, the transport improves in all strategies if we decrease the time that the vehicles persist in trying to follow a particular strategy when a route is blocked, namely, the mean flux increases, the individual travel times decrease, and the fluctuations of density in the streets decrease; consequently, deserting helps prevent traffic jams.

Random walk with priorities in communicationlike networks

We study a model for a random walk of two classes of particles (A and B). Where both species are present in the same site, the motion of A's takes precedence over that of B's. The model was originally proposed and analyzed in Maragakis et al. [Phys. Rev. E 77, 020103(R) (2008)]; here we provide additional results. We solve analytically the diffusion coefficients of the two species in lattices for a number of protocols. In networks, we find that the probability of a B particle to be free decreases exponentially with the node degree. In scale-free networks, this leads to localization of the B's at the hubs and arrest of their motion. To remedy this, we investigate several strategies to avoid trapping of the B's, including moving an A instead of the hindered B, allowing a trapped B to hop with a small probability, biased walk toward non-hub nodes, and limiting the capacity of nodes. We obtain analytic results for lattices and networks, and we discuss the advantages and shortcomings of the possible strategies.

Universality of flux-fluctuation law in complex dynamical systems

Recent work has revealed a law governing flux fluctuation and the average flux in complex dynamical systems. We establish the universality of this flux-fluctuation law through the following steps: (i) We derive the law in a more general setting, showing that it depends on a single parameter characterizing the external driving; (ii) we conduct extensive numerical computations using distinct external driving, different network topologies, and multiple traffic routing strategies; and (iii) we analyze data from an actual vehicle traffic system in a major city in China to lend more credence to the universality of the flux-fluctuation law. Additional factors considered include flux fluctuation on links, window size effect, and hidden topological structures such as nodal degree correlation. Besides its fundamental importance in complex systems, the flux-fluctuation law can be used to infer certain intrinsic property of the system for potential applications such as control of complex systems for improved performance.

Effect of network structure on phase transitions in queuing networks

Recently, De Martino et al. [J. Stat. Mech. (2009) P08023; Phys. Rev. E 79, 015101 (2009)] have presented a general framework for the study of transportation phenomena on random networks with annealed disorder. One of their most significant achievements was a deeper understanding of the phase transition from the uncongested to the congested phase at a critical traffic load on uncorrelated networks. In this paper, we also study phase transition in transportation networks using a discrete time random walk model. Our aim is to establish a direct connection between the structure of an uncorrelated random graph with quenched disorder and the value of the critical traffic load. We show that if the network is dense, the quenched and annealed formulas for the critical loading probability coincide. For sparse graphs, higher-order corrections, related to the local structure of the network, appear.

Reciprocal interactions out of congestion-free adaptive networks

In this paper we study the jamming transition in complex adaptive networks. We introduce an adaptation mechanism that modifies the weight of the communication channels in order to delay the congestion onset. Adaptivity takes place locally as it is driven by each node of the network. Surprisingly, regardless of the structural properties of the backbone topology, e.g., its degree distribution, the adaptive network can reach optimal functioning provided it allows a reciprocal distribution of the weights. Under this condition, the optimal functioning is achieved through an extensive network reshaping ending up in a highly reciprocal weighted network whose critical onset of congestion is delayed up to its maximum possible value. We also show that, for a given network, the reciprocal weighting obtained from adaptation produce optimal static configurations.

Control of epidemic spreading on complex networks by local traffic dynamics

Despite extensive work on traffic dynamics and epidemic spreading on complex networks, the interplay between these two types of dynamical processes has not received adequate attention. We study the effect of local-routing-based traffic dynamics on epidemic spreading. For the case of unbounded node-delivery capacity, where the traffic is free of congestion, we obtain analytic and numerical results indicating that the epidemic threshold can be maximized by an optimal routing protocol. This means that epidemic spreading can be effectively controlled by local traffic dynamics. For the case of bounded delivery capacity, numerical results and qualitative arguments suggest that traffic congestion can suppress epidemic spreading. Our results provide quantitative insight into the nontrivial role of traffic dynamics associated with a local-routing scheme in the epidemic spreading.

Information transfer dynamics in fixed-pathways networks

Most complex technological networks are defined in such a way that their global properties are manifested at a dynamical level. An example of this is when internal dynamical processes are constrained to predefined pathways, without the possibility of alternate routes. For instance, large corporation software networks, where several flow processes take place, are typically routed along specific paths. In this work, we propose a model to describe the global characteristics of this kind of processes, where the dynamics depends on the state of the nodes, represented by two possibilities: responsive or blocked. We present numerical simulations that show rich global behavior with unexpected emerging properties. In particular, we show that two different regimes appear as a function of the total network load. Each regime is characterized by developing either a unimodal or a bimodal distribution for the density of responsive nodes, directly related to global efficiency. We provide a detailed explanation for the main characteristics of our results as well as an analysis of the implications for real technological systems.