Connectivity In Such Networks Research Articles

Connectivity Status of Intuitionistic Fuzzy Graph and Its Application to Merging of Banks

Intuitionistic fuzzy graph theory is used to represent ambiguous networks, such as financial and social networks. The connectivity of such networks has a significant role in analyzing the network characteristics. This study investigates the connectivity status of vertices in an intuitionistic fuzzy graph. Few properties have been established. Some areas of applications are shown for merging different banks and finding the central affected nodes by any infectious diseases.

Dedicated Path Protection With Wavelength Aggregation in 5G Packet-Optical Xhaul Access Networks

Packet-optical xHaul transport networks are a promising solution for assuring low-cost connectivity between a large number of antennas, located at remote sites, and a central site (hub), in which traffic is gathered and baseband processing is performed, in dense 5G radio access networks (RANs). The demand for fiber connections in such networks can be reduced by means of optical add-drop multiplexers (OADMs), which allow to aggregate traffic from a number of remote sites onto a single optical transmission path. An important issue here is the protection of transmission paths since the failure of a single link may result in the loss of connectivity of a number of antennas. In this work, we focus on the problem of planning a survivable 5G packet-optical xHaul access network, in which the remote sites are connected with the hub by means of disjoint primary-backup paths. The network scenario is based on a passive wavelength division multiplexing (WDM) system in which the maximum transmission distance depends on the number of OADMs installed on the transmission path. In the solution proposed, network survivability is achieved using a dedicated path protection with wavelength aggregation (DPPWA) mechanism. The network planning problem is modeled and solved as an integer linear programming (ILP) problem. Numerical results indicate, among others, that cost savings of between 20%–40% can be achieved in lower traffic load scenarios due to the traffic aggregation by means of OADMs, and that the savings increase with the cost of dark fiber lease.

Modeling air-to-air communication networks in the North Atlantic region

The North Atlantic is one of the world’s airspaces accommodating a very high aircraft density while at the same time no radio coverage or radar surveillance is available. Beside satellite communication, one approach to enable data communication between aircraft and ground in the North Atlantic region is to establish ad-hoc networks build up by direct data links between the aircraft that are acting as communication nodes. In this paper we, therefore, present a modeling approach to model air traffic and ad-hoc networks in the North Atlantic region using up-to-date flight plans and trajectory modeling techniques and to assess the connectivity provided by such networks. Assuming an applicable set of ground stations that provide data transfer to and from this airborne network, we assess the connectivity by time-series analysis and in total for a set of different fractions of all aircraft assumed to be equipped with the necessary systems as well as for a variation of the air-to-air communication range. In addition, we present average link durations, average amounts of hops to reach ground and numbers of connected aircraft for the different scenarios and identify general relations between the different factors and metrics. We will show, that communication range and equipage fraction significantly influence the connectivity of such networks.

TECHNOLOGICAL MODEL AND SUSTAINABILITY ASSESSMENT METHODOLOGY OF THE MULTISERVICE COMMUNICATION NETWORK FUNCTIONING

The modern development of multiservice communication networks (MÑN) is characterized by the desire of operators to provide users with an unlimited range of services with guaranteed quality of service (QoS). QoS is determined by the stability of the network’s functioning, that is, its ability to function without errors. The existing routing algorithms in modern networks focus the main load on the most productive sections, while the rest of the network is used quite poorly. This is confirmed by the results of studies [1,2], in which the structure of modern global communication networks is assigned to the class of small-world graphs. Obviously, that the increased connectivity of such networks determines high indicators of its stability, but at the same time, this route redundancy is poorly used by existing routing protocols to provide QoS when transmitting different traffic (triple-play: audio, video, and data) [3, 4]. There is also no work devoted to the analysis of the possibilities of sharing different routes (route parallelism) for the implementation of QoS enhancement mechanisms in the MÑN. This article discusses the technological concept of a network with route parallelism in communication networks built on the basis of the IP protocol family.

Data-driven handover optimization in small cell networks

Since the advent of 1G through 5G networks, telecommunication industry has gone through phenomenal transformation in the way we communicate, we work, and we socialize. In dense or ultra-dense mobile communication networks, the users are very frequently handed over to other cells making seamless mobility a challenging and complex problem. Therefore, robust connectivity in such networks becomes a very critical issue. In this paper, we present a data-driven handover optimization approach aiming to mitigate the mobility problems including handover delay, early handover, wrong selection of target cell and frequent handover. The proposal is based on collecting the information from the network and developing a model to determine the relationship between the features drawn from the collected dataset and key performance indicator (KPI) expressed as the weighted average of mobility problem ratios. Handover design parameters- time to trigger and handover margin are optimized to improve KPI. The KPI estimation drawn on time to trigger and hysteresis margin design parameters is estimated through neural network multilayer perception method. It is established through simulation results that the proposed approach yields significantly improved handover performance mitigating mobility problem in ultra-dense cellular networks to notable extent.

Optogenetic Stimulation of Human Neural Networks Using Fast Ferroelectric Spatial Light Modulator—Based Holographic Illumination

The generation and application of human stem-cell-derived functional neural circuits promises novel insights into neurodegenerative diseases. These networks are often studied using stem-cell derived random neural networks in vitro, with electrical stimulation and recording using multielectrode arrays. However, the impulse response function of networks is best obtained with spatiotemporally well-defined stimuli, which electrical stimulation does not provide. Optogenetics allows for the functional control of genetically altered cells with light stimuli at high spatiotemporal resolution. Current optogenetic investigations of neural networks are often conducted using full field illumination, potentially masking important functional information. This can be avoided using holographically shaped illumination. In this article, we present a digital holographic illumination setup with a spatial resolution of about 8 µm, which suffices for the stimulation of single neurons, and offers a temporal resolution of less than 0.6 ms. With this setup, we present preliminary single-cell stimulation recording of stem-cell derived induced human neurons in a random neural network. This will offer the opportunity for further studies on connectivity in such networks.

A Multi-Layer Feedback System Approach to Resilient Connectivity of Remotely Deployed Mobile Internet of Things

Enabling the Internet of things in remote environments without traditional communication infrastructure requires a multi-layer network architecture. Devices in the overlay network such as unmanned aerial vehicles (UAVs) are required to provide coverage to underlay devices as well as remain connected to other overlay devices to exploit device-to-device (D2D) communication. The coordination, planning, and design of such overlay networks constrained by the underlay devices is a challenging problem. Existing frameworks for placement of UAVs do not consider the lack of backhaul connectivity and the need for D2D communication. Furthermore, they ignore the dynamical aspects of connectivity in such networks which presents additional challenges. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of underlay devices or unavailability of overlay devices due to failure or adversarial attacks. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. Results show that the proposed framework requires significantly lower number of aerial base stations to provide higher coverage and connectivity to remotely deployed mobile devices as compared to existing approaches.

Connectivity with backbone structures in obstructed wireless networks

In this work we consider a wireless ad-hoc network deployed on a finite street grid, where communication between nodes is disrupted by regularly spaced obstacles. The critical transmission range for connectivity in such networks grows with the size of the grid, which might impair the feasibility of low-power wireless technologies in large networks. We therefore analyze how the connectivity of such networks in sub-critical scenarios, where the transmission range is insufficient to establish connectivity, can be improved by introducing a global backbone with a set of access points. We formulate the problem of positioning a minimum number of access points, such that every connected component is covered by at least one access point, and refer to it as the Obstructed Wireless Network Backbone Cover Problem (OWN-BC). We prove that OWN-BC is NP-complete and present a 2-approximation algorithm to find solutions with guaranteed quality. Furthermore, we derive a lower bound on the probability of finding optimal solutions in random network scenarios. Finally, we perform a series of simulations to illustrate the performance of the approximation algorithm and characterize scenarios in which the proposed algorithm obtains optimal solutions in polynomial time.

Spectral and structural properties of random interdependent networks

Random interdependent networks consist of a group of subnetworks where each edge between two different subnetworks is formed independently with probability p. In this paper, we investigate certain spectral and structural properties of such networks, with corresponding implications for certain variants of consensus and diffusion dynamics on those networks. We start by providing a characterization of the isoperimetric constant in terms of the inter-network edge formation probability p. We then analyze the algebraic connectivity of such networks, and provide an asymptotically tight rate of growth of this quantity for a certain range of inter-network edge formation probabilities. Next, we give bounds on the smallest eigenvalue of the grounded Laplacian matrix (obtained by removing certain rows and columns of the Laplacian matrix) of random interdependent networks for the case where the removed rows and columns correspond to one of the subnetworks. Finally, we study a property known as r-robustness, which is a strong indicator of the ability of a network to tolerate structural perturbations and dynamical attacks. Our results yield new insights into the structure and robustness properties of random interdependent networks.

An efficient network selection algorithms in next-generation heterogeneous wireless networks

In the next-generation heterogeneous wireless networks, it is obligatory to consider the integration of various networks for providing the seamless connectivity to the mobile node roaming. While providing the seamless connectivity, a suitable network to choose from the given set of networks is the crucial issue. During vertical handoff, it is required to select the best network not only according to user preference but also the application, network and terminal contexts. The network selection algorithm plays a very important role in ensuring quality of service and seamless connectivity in such networks. Network selection in heterogeneous wireless networks has been widely studied by using various mathematical models, in which multiple attribute decision-making is one of the most popular models. This paper surveys the different network selection techniques available in the literature and explains a method for network selection which takes into consideration of multiple decision-making criteria for selecting the optimal network.

An efficient network selection algorithms in next-generation heterogeneous wireless networks

In the next-generation heterogeneous wireless networks, it is obligatory to consider the integration of various networks for providing the seamless connectivity to the mobile node roaming. While providing the seamless connectivity, a suitable network to choose from the given set of networks is the crucial issue. During vertical handoff, it is required to select the best network not only according to user preference but also the application, network and terminal contexts. The network selection algorithm plays a very important role in ensuring quality of service and seamless connectivity in such networks. Network selection in heterogeneous wireless networks has been widely studied by using various mathematical models, in which multiple attribute decision-making is one of the most popular models. This paper surveys the different network selection techniques available in the literature and explains a method for network selection which takes into consideration of multiple decision-making criteria for selecting the optimal network.

Critical Density in Adjustable-Orientation Directional Sensor Networks Using Continuum Percolation

In this paper, we consider adjustable-orientation directional sensor networks (ADODSNs) in which nodes are deployed based on Poisson Point Process and the orientation of sensor nodes is distributed between 0 and 2π independently and uniformly. Despite of other kinds of directional sensor networks, orientation of sensor nodes in ADODSNS could be adjusted after deployment by using an algorithm. We call this kind of sensor networks, adjustable-orientation directional sensor networks. We calculate the critical density of nodes for both sensing coverage and network connectivity in such networks using continuum percolation where field-of-view angles of sensors could be between 0 and π. Critical density is the infimum density of nodes to prepare barrier coverage. Also, extensive simulations have been conducted to represent the results. The findings of this research could be used for offline design of directional sensor networks and also its online algorithms such as scheduling, coverage and tracking.

Robustness and Algebraic Connectivity of Random Interdependent Networks

We investigate certain structural properties of random interdependent networks. We start by studying a property known as r-robustness, which is a strong indicator of the ability of a network to tolerate structural perturbations and dynamical attacks. We show that random /c-partite graphs exhibit a threshold for r-robustness, and that this threshold is the same as the one for the graph to have minimum degree r. We then extend this characterization to random interdependent networks with arbitrary intra-layer topologies. Finally we characterize the algebraic connectivity of such networks, and provide an asymptotically tight rate of growth of this quantity for a certain range of inter-layer edge formation probabilities. Our results arise from a characterization of the isoperimetric constant of random interdependent networks, and yield new insights into the structure and robustness properties of such networks.

Clustering predicts memory performance in networks of spiking and non-spiking neurons.

The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong negative linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

Joint Online Routing, Wavelength Assignment and Regenerator Allocation in Translucent Optical Networks

In translucent (or managed reach) WDM optical networks, regenerators are employed at specific nodes. Some of the connections in such networks are routed transparently, while others have to go through a sequence of 3R regenerators that serve as ?refueling stations? to restore their quality of transmission (QoT). We extend an online multicost algorithm for transparent networks presented in our previous study, to obtain an IA-RWA algorithm that works in translucent networks and makes use, when required, of the regenerators present at certain locations of the network. To characterize a path, the algorithm uses a multicost formulation with several cost parameters, including the set of available wavelengths, the length of the path, the number of regenerators used, and noise variance parameters that account for the physical layer impairments. Given a new connection request and the current utilization state of the network, the algorithm calculates a set of non dominated candidate paths, meaning that any path in this set is not inferior with respect to all cost parameters than any other path. This set consists of all the cost-effective (in terms of the domination relation) and feasible (in terms of QoT) lightpaths for the given source-destination pair, including all the possible combinations for the utilization of available regenerators of the network. An optimization function or policy is then applied to this set in order to select the optimal lightpath. Different optimization policies correspond to different IA-RWA algorithms. We propose and evaluate several optimization policies, such as the most used wavelength, the best quality of transmission, the least regeneration usage, or a combination of these rules. Our results indicate that in a translucent network the employed IA-RWA algorithm has to consider all problem parameters, namely, the QoT of the lightpaths, the utilization of wavelengths and the availability of regenerators, to efficiently serve the online traffic.

The Effect of Slow Electrical Stimuli to Achieve Learning in Cultured Networks of Rat Cortical Neurons

Learning, or more generally, plasticity may be studied using cultured networks of rat cortical neurons on multi electrode arrays. Several protocols have been proposed to affect connectivity in such networks. One of these protocols, proposed by Shahaf and Marom, aimed to train the input-output relationship of a selected connection in a network using slow electrical stimuli. Although the results were quite promising, the experiments appeared difficult to repeat and the training protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our ‘learning curves’ to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significantly improved stimulus response indeed. Furthermore, the protocol always induced functional connectivity changes that were much larger than changes that occurred after a comparable period of random or no stimulation. Finally, our data shows that stimulation at a fixed electrode induces functional connectivity changes of similar magnitude as stimulation through randomly varied sites; both larger than spontaneous connectivity fluctuations. We concluded that slow electrical stimulation always induced functional connectivity changes, although uncontrolled. The magnitude of change increased when we applied the adaptive (closed-loop) training protocol. We hypothesize that networks develop an equilibrium between connectivity and activity. Induced connectivity changes depend on the combination of applied stimulus and initial connectivity. Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state.

On the connectivity of wireless multi-hop networks with arbitrary wireless channel models

Considering a wireless multi-hop network where a total of n nodes are randomly, independently and uniformly distributed in a unit square in R2 and each node has a uniform transmission power, a fundamental problem is to investigate the connectivity of such networks. In this letter, we prove that the probability of having a connected network and the probability of having no isolated node asymptotically converges to the same value as n goes to infinity for an arbitrary wireless channel model satisfying certain intuitively reasonable conditions.

On-Line Optimal Wavelength Assignment in WDM Networks With Shared Wavelength Converter Pool

In this paper, we study on-line wavelength assignment in wavelength-routed WDM networks under both unicast and multicast traffic where nodes in the networks have wavelength conversion ability. Since wavelength converters are still expensive and difficult to implement, we consider the case where nodes in networks have only a limited number of converters that are shared by all input channels. We study the problem of setting up connections in such networks using minimum number of wavelength converters. For unicast traffic, we first study the problem of setting up a lightpath on a given link-path with minimum number of conversions. We give a simple algorithm that solves it in O(tk) time where t is the number of links on the path and k is the number of wavelengths per fiber, as compared to the best known existing method that needs to construct an auxiliary graph and apply the Dijkstra's algorithm. We also consider the problem of setting up a lightpath while using wavelength converters at nodes with fewer available converters only when necessary, and give an O(tk) time algorithm. We then generalize this technique to WDM networks with arbitrary topologies and give an algorithm that sets up an optimal lightpath network-wide in O(Nk+Lk) time, where N and L are the number of nodes and links in the network, respectively. We also consider multicast traffic in this paper. Finding an optimal multicast light-tree is known to be NP-hard and is usually solved by first finding a link-tree then finding a light-tree on the link-tree. Finding an optimal link-tree is also NP-hard and has been extensively studied. Thus, we focus on the second problem which is to set up a light-tree on a given link-tree with minimum number of conversions. We propose a new multicast conversion model with which the output of the wavelength converter is split-table to save the usage of converters. We show that under this model the problem of setting up an optimal light-tree is NP-hard and then give efficient heuristics to solve it approximately

Designing a Coherent Ecological Network for Large Mammals in Northwestern Europe

Abstract: In densely populated northwestern Europe, native large mammals are confronted with a very fragmented landscape, and most of the areas they inhabit are island‐like reserves threatened with total isolation from other reserves. The only way to counteract the threat of further decline in the numbers of large‐mammal species is to restore their habitats. The appropriate size of future reserves could be estimated from the habitat requirements of wild ungulates that are considered key species for ecosystem functioning. The species selected to guide the design of large nature reserves in northwestern Europe is the red deer ( Cervus elaphus) because of its widespread distribution, key role in ecosystem functioning, and home‐range size. We describe a network analysis of the Netherlands, Belgium, and adjacent parts of France and Germany, performed with the LARCH landscape ecology model, that was conducted in order to identify the structure of the ecological network for red deer and the spatial connectivity of the landscape. The resulting maps show areas that could support viable populations and indicate habitat areas that will support persistent populations only if they are in a network of linked habitats. The gaps and barriers that prevent connectivity in such networks guide the design of effective corridors to increase spatial connectivity. The results of our analysis can be used for policy decisions on nature conservation and spatial planning, and the method is applicable to other regions and species.