Large Communication Networks Research Articles

Software-Defined Space-Air-Ground Integrated Network Architecture with the Multi-Layer Satellite Backbone Network

Under the background of the rapid development of ground mobile communication, the advantages of high coverage, survivability, and flexibility of satellite communication provide air support to the construction of space information network. According to the requirements of the future space information communication, a software-defined Space-Air-Ground Integrated network architecture was proposed. It consisted of layered structure satellite backbone network, deep space communication network, the stratosphere communication network and the ground network. The SpaceAir-Ground Integrated network was supported by the satellite backbone network. It provided data relay for the missions such as deep space exploration and controlled the deep-space spacecraft when needed. In addition, it safeguarded the anti-destructibility of stratospheric communication and assisted the stratosphere to supplement ground network communication. In this paper, algorithm requirements of the congestion control and routing of satellite backbone protocols for heterogeneous users’ services were proposed. The algorithm requirements of distinguishing different service objects for the deep space communication and stratospheric communication network protocols were described. Considering the realistic demand for the dynamic coverage of the satellite backbone network and node cost, the multi-layer satellite backbone network architecture was constructed. On this basis, the proposed Software-defined Space-Air-Ground Integrated network architecture could be built as a large, scalable and efficient communication network that could be integrated into space, air, and ground.

The Roman introduction and exportation of animals into Tunisia: Linking archaeozoology with textual and iconographic evidence

The Mediterranean basin was the core of the Roman Empire. Large communication networks were constructed and maintained that enabled the trade and distribution of large number of products between distant territories. North Africa, and more specifically Tunisia, was an important trading area. However, little information is available about the animal trade. To characterize this activity and evaluate its economic importance, in this study we examine which species were introduced and exported during the Roman era in Tunisia using archaeozoological data and contextualizing them with written and iconographic sources. This combined approach shows that the Romans were responsible for introducing and exporting numerous animals. We have documented the introduction of commensal animals, such as black rat (Rattus rattus) and house mouse (Mus sp.), wild animals such as fallow deer (Dama dama) and hare (Lepus sp.), and domestic animals such as rabbit (Oryctolagus cuniculus) and cat (Felis catus). At the same time, North Africa supplied Rome with wild beasts such as lions, tigers, bears and camels. We suggest that the animal trade was an important economic activity for Tunisia not only for the export of wild animals but also for the import of animals that were mainly used for social purposes.

A large scale power communication network simulation system based on big graph database

The power communication network can be abstracted as a graph based on its topology. In this paper, we propose an approach to conduct simulations of power communication network based on its graph representation. In particular, the nodes and edges in the graph refer to the ports and channels in the grid topology. Different applications on the grid can be transformed into queries over the graph. Hence, in this paper, we build our grid simulation model based on the Neo4j graph database. We also propose a fault extension algorithm based on predicate calculus. Our experiment evaluations show that the proposed approach can effectively improve the efficiency of the power grid.

Performance Optimization of Optical Amplifiers for High Speed Multilink Optical Networks using Different Modulation Techniques

Abstract Next generation optical networks require large capacity communication networks for proper utilization of gain bandwidth of the networks. Optical amplifiers with wavelength division multiplexed (WDM) systems are the key technologies for the realization of these networks. In this paper, we have investigated the performance of the networks using different modulation techniques, such as return-to-zero (RZ), non-return-to-zero (NRZ) and duobinary. Attempts were taken to design 8 channels, 10 Gbps WDM network at channel spacing of 0.8 nm. An innovative model with EDFA-Raman amplifier is designed to examine the performance of the different modulation schemes. From the results, it is found that NRZ configuration gives maximum output power and good quality of communication as compared to other modulation schemes. Further, it is seen that with increase in channel dispersion from 2 to 4 ps/nm/km, the quality of the signal started decreasing.

Reliability Modeling and Analysis of Large Ship Communication Network Based on Genetic Algorithm

ABSTRACT Yang, L. and Guo, C., 2019. Reliability modeling and analysis of large ship communication network based on genetic algorithm. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in W...

Beating the Fundamental Rate-Distance Limit in a Proof-of-Principle Quantum Key Distribution System

With the help of quantum key distribution (QKD), two distant peers are able to share information-theoretically secure key bits. Increasing key rate is ultimately significant for the applications of QKD in lossy channel. However, it has proved that there is a fundamental rate-distance limit, named linear bound, which limits the performance of all existing repeaterless protocols and realizations. Surprisingly, a recently proposed protocol, called twin-field (TF) QKD can beat linear bound with no need of quantum repeaters. Here, we present the first implementation of TF-QKD protocol and demonstrate its advantage of beating linear bound at the channel distance of 300 km. In our experiment, a modified TF-QKD protocol which does not assume phase post-selection is considered, and thus higher key rate than the original one is expected. After well controlling the phase evolution of the twin fields travelling hundreds of kilometers of optical fibres, the implemented system achieves high-visibility single-photon interference, and allows stable and high-rate measurement-device-independent QKD. Our experimental demonstration and results confirm the feasibility of the TF-QKD protocol and its prominent superiority in long distance key distribution services.

Compact in‐memory representation of large graph databases for efficient mining of maximal frequent sub graphs

SummaryComplex networks have been used in many scientific disciplines like sociology, microbiology, and telecommunication to represent the interactions among them. Graphs are generally used for representing such complex networks. Mining significant frequent patterns from graph databases has been a challenging area of research. A number of sub graph mining algorithms have been proposed for finding frequent fragments in molecular databases. A very few algorithms have been proposed for mining frequent patterns from large communication networks. All these algorithms perform well on medium size networks and fail on very large graphs. The scalability of these algorithms has been an issue because of the enormous memory requirements and also due to the exponential number of frequent sub graphs possible. In this paper, we propose a compact way of representing graph databases and also use it in a maximal frequent sub graph mining algorithm. The algorithm is found to be efficient and scalable to very large graph databases.

The Fast Heuristic Algorithms and Post-Processing Techniques to Design Large and Low-Cost Communication Networks

It is challenging to design large and low-cost communication networks. In this paper, we formulate this challenge as the prize-collecting Steiner Tree Problem (PCSTP). The objective is to minimize the costs of transmission routes and the disconnected monetary or informational profits. Initially, we note that the PCSTP is MAX SNP-hard. Then, we propose some post-processing techniques to improve suboptimal solutions to PCSTP. Based on these techniques, we propose two fast heuristic algorithms: the first one is a quasilinear time heuristic algorithm that is faster and consumes less memory than other algorithms; and the second one is an improvement of a stateof-the-art polynomial time heuristic algorithm that can find high-quality solutions at a speed that is only inferior to the first one. We demonstrate the competitiveness of our heuristic algorithms by comparing them with the state-of-the-art ones on the largest existing benchmark instances (169 800 vertices and 338 551 edges). Moreover, we generate new instances that are even larger (1 000 000 vertices and 10 000 000 edges) to further demonstrate their advantages in large networks. The state-ofthe-art algorithms are too slow to find high-quality solutions for instances of this size, whereas our new heuristic algorithms can do this in around 6 to 45s on a personal computer. Ultimately, we apply our post-processing techniques to update the bestknown solution for a notoriously difficult benchmark instance to show that they can improve near-optimal solutions to PCSTP. In conclusion, we demonstrate the usefulness of our heuristic algorithms and post-processing techniques for designing large and low-cost communication networks.

A Review of Spiking Neuromorphic Hardware Communication Systems

Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation in a way that is inspired by concepts learned about the human brain. SNNs are artificial networks made up of neurons that fire a pulse, or spike, once the accumulated value of the inputs to the neuron exceeds a threshold. One of the most challenging parts of designing neuromorphic hardware is handling the vast degree of connectivity that neurons have with each other in the form of synaptic connections. This paper analyzes the neuromorphic systems Neurogrid, Braindrop, SpiNNaker, BrainScaleS, TrueNorth, Loihi, Darwin, and Dynap-SEL; and discusses the design of large scale spiking communication networks used in such systems. In particular, this paper looks at how each of these systems solved the challenges of forming packets with spiking information and how these packets are routed within the system. The routing of packets is analyzed at two scales: How the packets should be routed when traveling a short distance, and how the packets should be routed over longer global connections. Additional topics, such as the use of asynchronous circuits, robustness in communication, connection with a host machine, and network synchronization are also covered.

A Survey of Game Theory in Unmanned Aerial Vehicles Communications

Unmanned aerial vehicles (UAVs) can be deployed as wireless relays or aerial base stations to improve network connectivity and coverage in cellular networks. UAVs can also be used to significantly enhance the performance of mobile ad-hoc networks and wireless sensor networks. In the future, UAVs are expected to become an integral part of the fifth generation wireless networks as well as key enablers of the coming massive Internet of Things. However, there are still many challenging issues in designing architectures and deployment of UAV-based networks. To address the issues, game theory has recently been adopted as an effective tool for modeling and analyzing problems in UAV-aided networks. In this paper, we survey the applications of game theory in solving various UAV-assisted networks challenges. We first provide a brief introduction to wireless communications with UAVs and then introduce basic game theory concepts and their relation to wireless networks. We further present the classification and brief introduction to the games applied to solve problems in UAV-aided networks. We then provide a comprehensive literature review on game-theoretic techniques utilized in dealing with challenges in the UAV-based wireless networks. Finally, we introduce advanced distributed schemes for interference management in large UAV-assisted communication networks. This paper aims to provide readers with an understanding of UAV-aided networks in terms of their architecture, benefits, challenges, and various game theoretical solutions applied to these communications networks.

Social interactions in online eating disorder communities: A network perspective.

Online health communities facilitate communication among people with health problems. Most prior studies focus on examining characteristics of these communities in sharing content, while limited work has explored social interactions between communities with different stances on a health problem. Here, we analyse a large communication network of individuals affected by eating disorders on Twitter and explore how communities of individuals with different stances on the disease interact online. Based on a large set of tweets posted by individuals who self-identify with eating disorders online, we establish the existence of two communities: a large community reinforcing disordered eating behaviours and a second, smaller community supporting efforts to recover from the disease. We find that individuals tend to mainly interact with others within the same community, with limited interactions across communities and inter-community interactions characterized by more negative emotions than intra-community interactions. Moreover, by studying the associations between individuals’ behavioural characteristics and interpersonal connections in the communication network, we present the first large-scale investigation of social norms in online health communities, particularly on how a community approves of individuals’ behaviours. Our findings shed new light on how people form online health communities and can have broad clinical implications on disease prevention and online intervention.

Integer programming models for the routing and spectrum allocation problem

One of the most promising solutions to deal with huge data traffic demands in large communication networks is given by flexible optical networking, in particular the flexible grid (flexgrid) technology specified in the ITU-T standard G.694.1. In this specification, the frequency spectrum of an optical fiber link is divided into narrow frequency slots. Any sequence of consecutive slots can be used as a simple channel, and such a channel can be switched in the network nodes to create a lightpath. In this kind of networks, the problem of establishing lightpaths for a set of end-to-end demands that compete for spectrum resources is called the routing and spectrum allocation problem (RSA). Due to its relevance, this problem has been intensively studied in the last couple of years. It has been shown to be NP-hard (Christodoulopoulos et al. in IEEE J Lightw Technol 29(9):1354–1366, 2011; Wang et al. in IEEE J Opt Commun Netw 4(11):906–917, 2012) and several models and formulations have been proposed, leading to different solution approaches. In this work, we explore integer programming models for RSA, analyzing their effectiveness over known instances. We resort to several modeling techniques, to find natural formulations of this problem. Since integer programming techniques are known to provide successful practical approaches for several combinatorial optimization problems, the aim of this work is to explore a similar approach for RSA.

Efficient Information Flow Maximization in Probabilistic Graphs

Reliable propagation of information through large networks, e.g., communication networks, social networks or sensor networks is very important in many applications concerning marketing, social networks, and wireless sensor networks. However, social ties of friendship may be obsolete, and communication links may fail, inducing the notion of uncertainty in such networks. In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network. For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this data structure. Our evaluation shows that these heuristics lead to high quality solutions, thus yielding high information flow, while maintaining low running time.

Responsive Content-Centric Delivery in Large Urban Communication Networks: A LinkNYC Use-Case

Large urban communication networks such as smart cities are an ecosystem of devices and services cooperating to address multiple issues that greatly benefit end users, cities and the environment. LinkNYC is a first-of-its-kind urban communications network aiming to replace all payphones in the five boroughs of New York City (NYC) with kiosk-like structures providing free public Wi-Fi. We consolidate these networks with standalone edge cloud devices known as cloudlets and introduce geographically distributed content delivery cloudlets (CDCs) to store popular Internet content closer to end users; essential in environments with diverse and dynamic content interests. A content-centric and delivery framework is proposed leveraging NYC's population densities and CDCs for interest-based in-network caching. Analysis shows that although the adoption of multiple CDCs dramatically improves overall network performance, advanced caching policies are needed when considering increased content heterogeneity. Thus, we propose popularity-driven (pLFU) and cooperation-based (sLFU) caching policies at individual CDCs to account for user and content dynamics over time. The amalgamation of urban population densities, multiple CDC placements and smarter caching techniques help exploit the ultimate benefits of a content-centric urban communications network and dramatically improves overall network performance and responsiveness. Our proposed solutions are validated using LinkNYC as a use-case.

Infection prevention and control measures and tools for the prevention of entry of carbapenem-resistant Enterobacteriaceae into healthcare settings: guidance from the European Centre for Disease Prevention and Control

BackgroundInfections with carbapenem-resistant Enterobacteriaceae (CRE) are increasingly being reported from patients in healthcare settings. They are associated with high patient morbidity, attributable mortality and hospital costs. Patients who are “at-risk” may be carriers of these multidrug-resistant Enterobacteriaceae (MDR-E).The purpose of this guidance is to raise awareness and identify the “at-risk” patient when admitted to a healthcare setting and to outline effective infection prevention and control measures to halt the entry and spread of CRE.MethodsThe guidance was created by a group of experts who were functioning independently of their organisations, during two meetings hosted by the European Centre for Disease Prevention and Control. A list of epidemiological risk factors placing patients “at-risk” for carriage with CRE was created by the experts. The conclusions of a systematic review on the prevention of spread of CRE, with the addition of expert opinion, were used to construct lists of core and supplemental infection prevention and control measures to be implemented for “at-risk” patients upon admission to healthcare settings.ResultsIndividuals with the following profile are “at-risk” for carriage of CRE: a) a history of an overnight stay in a healthcare setting in the last 12 months, b) dialysis-dependent or cancer chemotherapy in the last 12 months, c) known previous carriage of CRE in the last 12 months and d) epidemiological linkage to a known carrier of a CRE.Core infection prevention and control measures that should be considered for all patients in healthcare settings were compiled. Preliminary supplemental measures to be implemented for “at-risk” patients on admission are: pre-emptive isolation, active screening for CRE, and contact precautions. Patients who are confirmed positive for CRE will need additional supplemental measures.ConclusionsStrengthening the microbiological capacity, surveillance and reporting of new cases of CRE in healthcare settings and countries is necessary to monitor the epidemiological situation so that, if necessary, the implemented CRE prevention strategies can be refined in a timely manner. Creating a large communication network to exchange this information would be helpful to understand the extent of the CRE reservoir and to prevent infections in healthcare settings, by applying the principles outlined here.This guidance document offers suggestions for best practices, but is in no way prescriptive for all healthcare settings and all countries. Successful implementation will result if there is local commitment and accountability. The options for intervention can be adopted or adapted to local needs, depending on the availability of financial and structural resources.

FMFinder: A Functional Module Detector for PPI Networks

Bioinformatics is an integrated area of data mining, statistics and computational biology. Protein-Protein Interaction (PPI) network is the most important biological process in living beings. In this network a protein module interacts with another module and so on, forming a large network of proteins. The same set of proteins which takes part in the organic courses of biological actions is detected through the Function Module Detection method. Clustering process when applied in PPI networks is made of proteins which are part of a larger communication network. As a result of this, we can define the limits for module detection as well as clarify the construction of a PPI network. For understating the bio-mechanism of various living beings, a detailed study of FMFinder detection by clustering process is called for.

CommViz: Visualization of semantic patterns in large social communication networks

We introduce CommViz, an information visualization tool that enables complex communication networks to be explored, exposing trends and patterns in the data at a glance. We adapt a visualization approach known as hive plots to reflect the semantic structure of the networks, a generalization we call semantic hive plots. The method efficiently organizes and provides insight into complex, high-dimensional communication data such as email and messages on social media. We present the architecture of the CommViz tool and its application to the Enron email corpus as a case study, demonstrating how the structure of the visualization enables investigation of patterns and relationships in a large set of messages. We also provide a user study performed with Amazon Mechanical Turk that shows the value of the tool for certain complex data interrogations and further show how the incorporation of semantic structure on semantic coordinates can also be applied to parallel coordinates visualization. The integration of the social network characteristics with semantic attributes of the underlying data in a single visualization is, to our knowledge, a novel contribution of the work. The tool can be accessed at http://commviz.eng.unimelb.edu.au . Code is available at https://bitbucket.org/readbiomed/commviz . The Enron email corpus is available from http://bailando.sims.berkeley.edu/enron_email.html .

Structural Controllability and Observability in Industrial N 2 State Charts Applied to a Supervisory Servo Controller

This paper presents that the structural controllability and observability can be used for a class of discrete event systems modeled by industry-standard N-squared diagrams. The main results of this paper provide analytical assessment of large scale industrial system properties before the software simulation and hardware demonstration; therefore it offers immense savings in verification time and cost. The dynamics of N-squared diagrams are represented by linear time-invariant systems over the Boolean algebra. Structural controllability and structural observability of discrete event systems are transformed to “standard” controllability and observability problems in traditional linear systems over real numbers. The rank of the controllability and observability matrices determine not only the structural controllability and observability, but also which discrete nodes cannot be reached by the initial states and which discrete states have no outgoing paths to the output nodes, respectively. This rank condition is extremely easy to be verified through computer software, such as MATLAB, it can be used in large scale industrial systems or communication networks.

Poisson Hypothesis for Open Networks at Low Load

We study large communication networks of the mean-field type. The input flows to the nodes of the network are supposed to be stationary and with low rate. We show that such a network is ergodic, i.e. it goes to the stationary state, which does not depend on the initial state of the network. This is in contrast with the high load regime, when the large time behavior of the network might depend on its initial state. Our technique is based on the coupling construction, which couples two Non-Linear Markov Processes.

A Fuzzy Logic Trust Model for Secure Routing in Smart Grid Networks

Smart grids require communication networks to convey sensing and control data for improving the efficiency of energy generation, transmission, and delivery. As a result, smart girds become vulnerable to various types of cyber-attacks. Trust models were recognized as one of the important methods of defending a large communication network against malicious cyber-attacks. In this paper, a fuzzy logic trust model is proposed to detect untrusted nodes in smart grid networks, and compared with an existing model to show its advantages. Using this proposed model, both the routing efficiency and the detection rate for all types of considered malicious behaviors can be improved. In comparison with the existing lightweight and dependable trust system model, the proposed model improves the packet dropping rate by up to 90% when the percentage of malicious nodes is less than 25%, as verified by simulations.