Variety Of Network Topologies Research Articles

Dual‐failure restorability analysis of span‐restorable meta‐mesh networks

AbstractThe span‐restorable meta‐mesh model was previously designed as a cutting‐edge technique that enhanced the spare capacity efficiency in low average nodal degree networks. In this technique, lightpaths that fully traverse chains of degree‐2 nodes are provided with a logical express bypass span allowing a distinction between the internal and external working flow capacity that transit the chain. In the event of span failure, lightpaths which would normally traverse the chain in its entirety are allowed to fail back to its anchor nodes such that only the intrachain flow requires allocation of spare capacity. Previous work on the meta‐mesh design considered only single failure restorability. The work herein analyzes dual span failure situations by developing two new integer linear programming models. The first model provides the minimum total cost of designing a meta‐mesh network capable of withstanding dual span failure scenarios. The second model offers a maximization of the dual failure restorability by minimizing the number of nonrestored working capacities with a given limit of total spare capacity investment. Experiments are performed on six master test‐case networks of various topologies and scales.

Neural network‐based routing protocol for opportunistic networks with intelligent water drop optimization

SummaryOpportunistic Networks (OppNets) is a system of wirelessly connected nodes in a varying network topology. Routing in OppNets is a challenge. To overcome the problem of routing, an intelligent dynamic strategy to select next best node for forwarding a message is required. This paper proposes an intelligent routing mechanism based on Intelligent Water Drop (IWD) Algorithm which is used in tandem with Neural Networks (NNs) as an optimization technique to solve the problem of routing in such networks. The nature–inspired IWD algorithm provides robustness, whereas the neural network base of the algorithm helps it to make intelligent routing decisions. The weights in the Neural Network model are calculated by IWD Algorithm using training data consisting of inputs that are characteristic parameters of nodes, such as buffer space, number of successful deliveries and energy levels along with transitive parameters such as delivery probabilities. The proposed protocol Intelligent Water Drop Neural Network (IWDNN) is compared with other protocols that use similar ideologies such as MLProph, K‐nearest neighbour classification based routing protocol (KNNR), Cognitive Routing Protocol for Opportunistic Network (CRPO), and Inheritance Inspired Context Aware Routing Protocol (IICAR), as well as the standard protocol Prophet. IWDNN is shown to outperform all other protocols with an average message delivery ratio of 60%, which is a significant improvement of over 10% in comparison to other similarly conceived algorithms. It has one of the lowest latency among the protocols studied, in a range of 3000 to 4000 s, and incurs comparably low overhead costs in the range of 15 to 30. The drop ratios are one of the lowest, staying near six and approaching zero as buffer size is increased. Average amount of time a message stayed in the buffer was the lowest, with a mean of 1600 s.

Nodal-pressure-based heating flow model for analyzing heating networks in integrated energy systems

District heating technology has prompted the development of integrated energy systems, where energy flow models have been widely used. However, as heating networks become complicated, the traditional mass flow model fails to provide efficient solutions. This study proposes a novel heating flow model based on nodal pressure in heating networks. First, transmission models based on nodal pressure are formulated. Subsequently, nodal conservation models of mass and heating flow are developed. Further, the transmission and nodal conservation models are combined, and thereby nodal pressure and user load are directly correlated. As the number of variables depends only on the nodes, the resulting model is more efficient at adapting to new network connections when the nodes do not change. Moreover, as loop pressure conservation equations are omitted in this model, fewer iterations are required. Owing to the small relative fluctuations of nodal pressure, initial iteration values can be set more effectively in the solution of the proposed model. A case study was conducted, and it was demonstrated that compared with the traditional mass flow model, the proposed model is more effective in adapting to varying network topologies, and the computational burden is significantly reduced. Moreover, the proposed model can be efficiently applied for analyzing energy transmission loss as well as system planning in integrated power–gas–heat energy systems.

Research on adaptive beacon message broadcasting cycle based on vehicle driving stability

SummaryEnsuring smooth communication by fixed‐cycle message beaconing in vehicular environments is necessary to address vehicles safety. However, fixed‐cycle beacon messages cannot accommodate the characteristics of fast vehicle speeds and variable network topologies and can cause problems such as channel congestion when traffic density is too high. Therefore, in order to realize safe and reliable information transmission between vehicles, this paper proposes a strategy for adaptive update of beacon message cycle based on vehicle driving stability. It is based on two rules: one is that the vehicle position prediction error is defined as an unstable vehicle, and the small error is defined as a stable vehicle; and the other is that the adaptive beacon message cycle is ranged, which is determined according to the channel load capacity. The experimental results show that the strategy can effectively avoid the channel congestion problem and improve the driving safety of the vehicle. Compared with the fixed‐cycle beacon message, the communication delay is reduced by about 10%, the packet loss is reduced by about 22%, and the energy consumption is reduced.

Double-Edge Embedding Based Provenance Recovery for Low-Latency Applications in Wireless Networks

A number of applications in next-generation multi-hop networks, e.g., vehicular networks, impose low-latency requirements on data transmission thereby necessitating the underlying relays to introduce negligible delay when forwarding the packets. While traditional relaying techniques such as amplify-and-forward protocols may help the packets to satisfy latency-constraints, such strategies do not facilitate the destination in learning the path traveled by the packets, which in turn could be used for either learning the topology of the network or detecting security threats on the network. In addition to low-latency constraints, vehicular networks also result in variable network topology owing to the mobility of the nodes, which in turn imposes additional challenges to the destination in learning the path traveled by the packets. Thus, with potential applications to vehicular networks, we address the problem of designing provenance embedding algorithms that reduce the delays on the packets and yet assist the destination in determining the path traveled by the packets with no knowledge of the network topology. We propose a new class of provenance embedding techniques, referred to as double-edge (DE) embedding techniques, wherein a subset of the relay nodes in the path strategically skip the provenance embedding process to reduce the delays on the packets. Using fixed-size bloom filters as tools to implement the double-edge embedding ideas, first, we derive upper bounds on the error-rates of the DE embedding techniques so that the parameters of the bloom filter can be chosen to facilitate provenance recovery within a given quality of service. Subsequently, we present experimental results on a test bed of XBee devices and Raspberry Pis to demonstrate the efficacy of the proposed techniques, and show that the DE embedding techniques offer latency benefits upto 17 percent along with remarkable reduction in error-rates in comparison with the baselines. We also present a security analysis of the proposed provenance embedding methods to asses their vulnerabilities against various attacks including impersonation threats.

Dynamic Reference Selection-Based Self-Localization Algorithm for Drifted Underwater Acoustic Networks

Self-localization has become one of the major areas of research in drifted underwater acoustic networks (DUANs) since many applications are based on the knowledge of nodes’ positions. However, self-localization for DUANs faces two main challenges: the insufficient anchors and the varying network topology. Both affect the localization performance seriously. In this paper, we focus on these two challenges and propose a dynamic reference selection-based self-localization algorithm for DUANs (DRSL) to improve the localization performance. First, an optimal reference selection scheme is presented to solve the insufficient anchors’ problem. The selected optimal reference node can not only assist the insufficient anchors in accomplishing the localization procedure, but also obviously increase the localization accuracy. Based on the proposed optimal reference selection scheme, a dynamic reference selection-based self-localization algorithm is proposed to solve the topology changing problem. The proposed algorithm can improve the localization performance for DUANs significantly by selecting the reference node dynamically according to the predicted network topology, which is more suitable for DUANs with mobile sensor nodes. Simulation results show that the proposed DRSL algorithm can increase the localization accuracy greatly with insufficient anchor nodes and varying network topology. In addition, DRSL algorithm also has a lower communication cost than other anchor-free approaches, which distinctly demonstrates the advantages of the proposed DRSL algorithm.

New approach for the probabilistic power flow of distribution systems based on data clustering

The growing popularity of renewable-based generations along with loads fluctuation and network topology variation has exposed distribution systems to high uncertainties, causing difficulties in operating and planning decisions. In addition, the correlation among various uncertain variables has introduced more complexity to this problem. The probabilistic assessment of power systems with various uncertain variables and with any correlation between them can be efficiently handled by Monte–Carlo simulation (MCS) method, but the calculation burden in this method is heavy and thus it is not appropriate in online applications. Keeping the accuracy of the results, data clustering techniques can be efficiently substituted for this method with much less calculation time and burden. In this study, two methods based on data clustering which can consider the correlation between different variables in a straightforward manner are presented for the probabilistic power flow of distribution systems. In order to demonstrate the efficiency of the proposed methods, IEEE 37 node test feeder and IEEE 123 node test feeder were selected as the case study. The results obtained by the proposed methods were compared with those of the MCS method in terms of accuracy and calculation time.

Robust PI controller design for frequency stabilisation in a hybrid microgrid system considering parameter uncertainties and communication time delay

One of the major issues in the operation of an islanded microgrid is the frequency deviations caused by the variation of power production coming from non-dispatchable renewable energy sources. Using an appropriate control system may allow the system to deal with the frequency deviation and allow the system frequency to be restored to its rated value. A wide-area system frequency controller sends a control signal to each of the dispatchable generating sources to enforce the frequency control by using communication channels. However, the communication channels are susceptible to the time delays, which affect the performance of the frequency control system and potentially the system security. Also, the system parameters are uncertain due to the error in the modelling, variation in network topology, generations, loads etc. This study proposes a robust proportional and integral (PI) system frequency controller based on Kharitonov theorem (KT) considering the time delay of the communication system and the system parametric uncertainties. Simulation results demonstrate that the KT-based controller model has enhanced robustness compared to the conventional approaches of controller design.

Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for Quantitative Microbiome Data.

High-throughput microbial sequencing techniques, such as targeted amplicon-based and metagenomic profiling, provide low-cost genomic survey data of microbial communities in their natural environment, ranging from marine ecosystems to host-associated habitats. While standard microbiome profiling data can provide sparse relative abundances of operational taxonomic units or genes, recent advances in experimental protocols give a more quantitative picture of microbial communities by pairing sequencing-based techniques with orthogonal measurements of microbial cell counts from the same sample. These tandem measurements provide absolute microbial count data albeit with a large excess of zeros due to limited sequencing depth. In this contribution we consider the fundamental statistical problem of estimating correlations and partial correlations from such quantitative microbiome data. To this end, we propose a semi-parametric rank-based approach to correlation estimation that can naturally deal with the excess zeros in the data. Combining this estimator with sparse graphical modeling techniques leads to the Semi-Parametric Rank-based approach for INference in Graphical model (SPRING). SPRING enables inference of statistical microbial association networks from quantitative microbiome data which can serve as high-level statistical summary of the underlying microbial ecosystem and can provide testable hypotheses for functional species-species interactions. Due to the absence of verified microbial associations we also introduce a novel quantitative microbiome data generation mechanism which mimics empirical marginal distributions of measured count data while simultaneously allowing user-specified dependencies among the variables. SPRING shows superior network recovery performance on a wide range of realistic benchmark problems with varying network topologies and is robust to misspecifications of the total cell count estimate. To highlight SPRING's broad applicability we infer taxon-taxon associations from the American Gut Project data and genus-genus associations from a recent quantitative gut microbiome dataset. We believe that, as quantitative microbiome profiling data will become increasingly available, the semi-parametric estimators for correlation and partial correlation estimation introduced here provide an important tool for reliable statistical analysis of quantitative microbiome data.

Resistances between two nodes of a path network

Considerable progress has recently been made in the development of techniques to exactly determine two-point resistances in networks of various topologies. Given n positive integers m1,m2,…,mn, let P[mi]1n be the network with node set V=V1∪V2∪…∪Vn, where Vi∩Vj=∅ if i ≠ j and |Vi|=mi, and with edge set E={uv|u∈Vi,v∈Vi+1,i=1,2,…,n−1}. In this paper, using triangle-star transformation and the principle of substitution, we express arbitrary two nodes resistances of a path network in terms of the number of their nodes.

Distributed optimal wind farm control for fatigue load minimization: A consensus approach

In this paper, we introduce a distributed approach to optimal control of wind farms (WFs). Our control approach enables wind generators (WGs) to dynamically dispatch and regulate their power outputs to optimal equilibria that result in minimum total fatigue loading and aggregate power output that matches a given reference. The underlying control algorithm requires WGs to only update and exchange minimal local nonsensitive information via an arbitrary peer-to-peer communication network and is characterized by: (a) computationally efficiency, as the computational effort is uniformly distributed among all WGs, (b) resilience with respect to communication network topology variations and, (c) privacy preservation, as WGs only exchange nonsensitive information. We establish convergence of the underlying distributed algorithm under mild conditions in the communication network connectivity and control gains. Finally, we corroborate the effectiveness of the proposed distributed control approach via numerical simulations and show how WGs, by adopting the proposed controllers, can regulate their total power output on demand while ensuring that the harmful fatigue loads that they experience are kept at minimal levels.

Oscillating droplet trains in microfluidic networks and their suppression in blood flow

Droplets forming sequences in simple microfluidic networks are known to exhibit complex behaviours, but their dynamics are yet to be probed in channels long enough to accommodate many droplets simultaneously. Here we show that uniform sequences of liquid droplets flowing through microfluidic networks can spontaneously form ‘trains’ that periodically exchange between different branches of the network. Such system-wide oscillations do not rely on direct droplet–droplet interactions, are common to networks of various topologies, can be controlled or eliminated by adjusting network dimensions and can synchronize into larger flow patterns. The oscillations can also be suppressed via droplet collisions at diverging junctions. This mechanism may explain why red blood cells in microcapillaries exhibit only low-amplitude oscillations, preventing dangerous local hypertension or hypoxia that might otherwise ensue. Our findings are substantiated by a theoretical model that treats droplets as sets of moving points in one-dimensional ducts and captures the dynamics of large droplet ensembles without invoking the microscopic details of flows in or around the droplets. For blood flow, this simplified description offers more realistic estimates than continuous haemodynamic models, indicating the relevance of the discrete nature of blood to the excitation of oscillations. Droplet sequences in microfluidic networks are shown to form trains that oscillate between branches of the network. Control of this effect suggests a mechanism by which red blood cells might avoid certain pathologies by minimizing oscillations.

Dynamical rewiring in adaptive weighted heterogeneous networks

When virus spreads in a network, individuals may adaptively change their contact relationships to reduce risk of infection. In order to describe the interaction between a time varying network topology and the dynamics of the nodes, here we propose a susceptible–infected–susceptible (SIS) model on a weighted adaptive heterogeneous network where the dynamics of and on the networks are described. Rewiring strategies are designed to inhibit the epidemic spreading. Our results show that network topology and individual behaviors have significant influence on virus spreading in a weighted adaptive heterogeneous network. The adaptive rewiring process during the dynamics can effectively inhibit virus spreading. Large dispersion of weights can significantly decrease the final epidemic size. Moreover, our proposed rewiring strategies based on the individual spontaneous behaviors are effective for the inhibition of epidemics.

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

The vehicular edge computing (VEC) system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states and computing workloads. These uncertainties bring extra challenges to task offloading. In this work, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning-based task offloading (ALTO) algorithm based on the multi-armed bandit (MAB) theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.

PTCCR: A Path Transmission Costs-Based Multi-Lane Connectivity Routing Protocol for Urban Internet of Vehicles

In Internet of Vehicles (IoV), high vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, variation of network topology and constraints for routing protocols. Besides, with vehicular positions and street-level digital maps available, the intersection-based geographic routing becomes indispensable considering its ability for avoid forwarding packets through segments with low network density and high scale of network disconnections. In this paper, considering the benefits of intersection-based routing and challenges of high dynamic IoV, a Path Transmission Costs-based Multi-lane Connectivity Routing protocol (PTCCR) is proposed with the help from intersection nodes and/or neighboring nodes. First, we investigate the multi-lane connectivity based on vehicular speed under free-flow state for various types of road sections. Second, A Path Transmission costs (PTC) measurement mechanism is proposed considering the impact of the sequence of selected sections or intersections on the routing performance. After that, the PTC of sent packets is quantitatively analyzed and used as the path selection metric. Finally, the path with the largest multi-lane connectivity and lowest PTC is selected as the optimal path taking the transmission direction, neighbor's location and destination position into account. Numerical results show that our proposed PTCCR outperforms two state-of-art routings, i.e., the real-time intersection-based segment aware routing protocol (RTISAR) and Reliable Traffic Aware Routing protocol (RTAR), in terms of packet delivery ratio, average end-to-end delay and communication overhead.

A dynamic evolution model of balanced energy consumption scale-free fault-tolerant topology based on fitness function for wireless sensor networks

The network life can be prolonged to some extent by the scale-free topology which has strong fault-tolerance for the random failure of nodes. However, the unevenness of the scale-free topological structure can result in the imbalance of consumption of network. In order to resolve the problem, a dynamic evolution model of consumption of balanced energy with scale-free fault-tolerant topology based on fitness function for wireless sensor networks is proposed. First, the effect of nodal energy and the distance between the nodes on the network life is analysed as per the energy consumption model. Thereafter, the ratio between the residual energy of the node and the distance between nodes is used as the fitness function along with the dynamic behaviour of link and variations in network topology. Thus, a fault-tolerant topology model with scale-free properties is obtained. The simulation results indicate that the proposed model can not only balance the energy consumption of the nodes and the network by prolonging the life of network, but can enhance the fault tolerance and the invasion ability of the network by comparing with Barabasi-Albert model (BA), energy aware evolution model (EAEM) and fault-tolerant topological model with energy consumption and load (FTEL).

The Adaptive and Robust Power System Protection Schemes in the Presence of DGs

In recent years, using distributed generations (DGs) has increased in order to compensate the load and consumption growth. Although connecting DG resources to power system has many advantages, it might cause some protective problems such as mis-coordination between network protection devices, due to the changing in the short circuit level of power system. In this paper, two method for restoring the coordination of relays in the presence of DGs by using FCLs are proposed. In both methods, different criteria, such as considering the variation of network topology, optimizing the value and number of FCLs are considered to propose a comprehensive and adaptive protection method. In the first method, an adaptive protection scheme by using FCLs is proposed to active and deactivate FCLs in different topologies to restore the coordination of relays. In addition, the second method proposes a robust method which considers all possible situations and topologies of the power system to maintain the coordination of power system during fault. Finally, the proposed robust and adaptive protection scheme has been compared in terms of cost, the operation time of relays and computation time, and based on the aims of operators in a power system one of these method can be implemented, The proposed methods are implemented on IEEE 14-bus standard network, and all possible topologies of the mentioned network are considered. Simulation results show that the proposed method can maintain the coordination of overcurrent relay protection in the presence of DGs and topology changes. In other words, the proposed method is robust against topologies changes.

A dynamic evolution model of balanced energy consumption scale-free fault-tolerant topology based on fitness function for wireless sensor networks

The network life can be prolonged to some extent by the scale-free topology which has strong fault-tolerance for the random failure of nodes. However, the unevenness of the scale-free topological structure can result in the imbalance of consumption of network. In order to resolve the problem, a dynamic evolution model of consumption of balanced energy with scale-free fault-tolerant topology based on fitness function for wireless sensor networks is proposed. First, the effect of nodal energy and the distance between the nodes on the network life is analysed as per the energy consumption model. Thereafter, the ratio between the residual energy of the node and the distance between nodes is used as the fitness function along with the dynamic behaviour of link and variations in network topology. Thus, a fault-tolerant topology model with scale-free properties is obtained. The simulation results indicate that the proposed model can not only balance the energy consumption of the nodes and the network by prolonging the life of network, but can enhance the fault tolerance and the invasion ability of the network by comparing with Barabási-Albert model (BA), energy aware evolution model (EAEM) and fault-tolerant topological model with energy consumption and load (FTEL).

Chimera States in Three Populations of Pendulum-Like Elements with Inertia

The aim of this study is to investigate the chimera states in three populations of pendulum-like elements with inertia in varying network topology. Considering the coupling strength between oscillators within each population is stronger than the inter-population coupling, we search for the chimera states in three populations of pendulum-like elements under the ring and the chain structures by adjusting the inertia and the damping parameter. The numerical evidence is presented showing that chimera states exist in a narrow interval of inertia in ring and chain structures. It is found that chimera states cease to exist with the decreasing of damping parameter. Furthermore, it is revealed that there is a linear relationship between the inertia (m) and damping parameter threshold (eth) in the two network structures.

A graph-based modelling methodology for high-pressure networks applied on waterjet machining

This paper proposes a graph-based methodology that models high-pressure networks of various topologies. Therefore, a mathematical modelling of a supply network for waterjet machining will be introduced. High-pressure components are assigned to homogeneous segments, each representing a local pressure state as a differential equation. Segments are subsequently interconnected along the fluid flow path as an algebraic equation that allocates a fluid flow to the interconnections, resulting in a lumped parameter model. For this purpose, a graph network description has been used to approximate the spatially distributed high-pressure system. In this way, the proposed methodology offers a flexible modelling to cope with different network topologies. Moreover, a variable fluid compressibility has also been introduced so that a wide operating range can be included. This modelling methodology has been applied to a supply network for waterjet machining. The resulting mathematical model has been verified by measurements from a test bench with a pressure range of 100−400 MPa. It was shown that a variable fluid compressibility improves the model’s accuracy and that modelling errors can be reduced in comparison to other existing methodologies.